## 4 Deep Learning
Deep generative adversarial networks (DGAN) were used to provide virtual training samples for the suggested wireless sensing system based on deep learning. The case study of wireless gesture recognition established its efficacy. Remote sensing is another important area of DL application, with an exponential rise in papers published on the subject in recent years ([PERSON] and [PERSON] et al. [70]). Supervised CNNs give optimal performances in the direct classification of hyperspectral images in the spectral domain, as obtained by [PERSON] and [PERSON] et al. [71]. The adopted CNN model has only one convolutional level, since authors verified that the typical CNN with two convolutional layers is actually not applicable for hyperspectral data. According to experimental results based on multiple hyperspectral image datasets, the suggested method produced high classification performance. CNNs analyze image-based patterns and are ineffective at simulating temporally oriented events. On the other hand, R-CNNs are particularly well suited to model temporal changes in data. They perform temporal analysis of events in time-sequence applications, such as language and speech recognition, when given sequential inputs. The history of the sequence is stored in a state vector in the hidden units of an R-CNN, which processes one input element at a time. Back-propagation is utilized to train an R-CNN because the outputs of the hidden neurons at each step time are analogous to the outputs of various neurons in a deep multilayer network. [PERSON] and [PERSON] et al. [72] use a deep R-CNN in the field of HAR to identify human behaviors (such as sitting, standing, and walking) from data collected by wearable body sensors. In the study two publicly available datasets, MHEALTH (mobile health) ([PERSON] and [PERSON] et al. [73]) and PUC-Rio are used, as well as the AReM (activity recognition system based on multi-sensor data fusion) dataset gathered by the authors. The suggested method is based on data fusion from many wearable sensors, including an electrocardiogram (ECG), an accelerometer, and a magnetometer. Next, using kernel principal component analysis (KPCA), features are retrieved, and then a deep R-CNN is applied to recognize behavior. Controlling HMI devices or artificial limbs frequently involves the detection and classification of human movements. According to [PERSON] and [PERSON] et al. [74], a R-CNN is a promising decoder for classifying hand movements based on the combination of complicated time-series EMG signals and acceleration data. Remote sensing also uses R-CNN. [PERSON] and [PERSON] et al. [75] developed a super-resolution method based on an R-CNN architecture to produce a high-resolution image from a succession of low resolution satellite photographs. In order to learn long-term dependencies, [PERSON] and [PERSON] [76] modified the recurrent neural network (RNN) and created the long short-term memory (LSTM). The LSTM employs a self-feeding loop in its inner layers that may learn time-based correlations, combining knowledge from previous inputs into the analysis of present inputs. Both spatial and temporal information may be extracted from data thanks to the combined strength of CNN and LSTM. A CNN-LSTM was used by [PERSON] et al. in [77] to determine the position of a gas source (GSL) in an outdoor environment using a variety of stationary sensors (sensor network). In the investigation, thirty metal oxide (MOX) gas sensors that are commercially available and one ultrasonic anemometer were used, and data from the gas sensor array were arranged in a series of monochrome images to create a visual learning challenge for GSL. According to [PERSON] et al. [78], a deep CNN-LSTM provides reliable findings for predicting air pollution in the field of earth environmental monitoring. Ground-based pollution sensors are used in the solution, and the sensor data are recast into a modified pseudo-image to enable the usage of deep 1D CNN and LSTM. [PERSON] and [PERSON] et al.