## 4 Deep Learning
The classification is based on camera images captured in pre-defined collection points using Samsung Galaxy S8 rear camera. For recognizing scenes, CNN has a prediction accuracy of 91.04%. The search space in a geomagnetic database used for localization is further reduced using the identified scene. [PERSON] and [PERSON] et al. [62] use image processing and a CNN-based technique to determine UV intensity. They created a wearable UV sensor out of PDMS and photochromic material. Images from a cell phone were used to construct the dataset, and the sensor changes color when exposed to UV radiation. When a CNN was trained to measure UV intensity, the influence of ambient light was considerably diminished, yielding an identification rate of more than 90% under various ambient light circumstances. [PERSON] et al. [63] presented a comprehensible fuzzy fusion method to combine the output of CNN models that could assess the relevance of each classifier by looking at the interaction index between each classifier. Additionally, SoftPool and Mish activation features were added to conventional CNNs to improve their capacity for feature extraction. An experimentally collected dataset and an artificially generated fault bearing dataset are used to evaluate the performance of the suggested model and assess its capacity to extract features. In the health monitoring of industrial systems, DL is extensively adopted, as DL-based fault diagnosis methods achieve better results than traditional ML methods. Bearing fault detection classification and localization is a problem in which CNNs obtained very positive performances ([PERSON] and [PERSON] et al. [64]). [PERSON] and [PERSON] et al. [65] proposed a deep residual convolutional neural network (DR-CNN) with gray-scale pictures obtained by a multi-sensor data (multiple 3-axis accelerometers) as input data to address the problem of bearing fault diagnostics with multi-sensor data. The CNN degrades as the network depth reaches a particular level, but certain connections in the residual network skip some of the CNN structure's layers, making it simple for parameter gradients to spread from the output layer to the lower levels. Another area, in which accelerometer signals are highly adopted is the biomedical field, such as for human activity recognition (HAR). In [PERSON] and [PERSON] [66], the authors apply a deep CNN for HAR using a publicly available dataset ([PERSON] et al. [67]), which contains raw data from four tri-axial wearable accelerometers. The suggested approach is evaluated against other conventional classifiers, such as decision trees, random forest, support vector machines (SVM), and k-Nearest Neighbors (kNN). A classification accuracy of 100% is achieved, with the great advantage of eliminating the need for a pre-processing activity. A combination of a CNN and a deep convolutional generative adversarial network (DCG), whose acronym is DCG-CNN, is proposed by [PERSON] and [PERSON] [68] for gas sensor condition monitoring to prevent fault; in the specific case, the gas is hydrogen. A DCG combines a CNN with a generative adversarial network (GAN), whose purpose is to produce fresh samples of data from the available data with the same statistical properties, enhancing defect detection precision when imbalanced data samples are present. The following steps make up the method: the DCG approach is used to construct synthetic 2D grey images of sensor fault signals from 1D hydrogen sensor fault signals; the experimental signal and the synthetic signal are mixed to balance the training dataset. A CNN is trained and evaluated using the entire dataset. It has been discovered that deep learning has great promise for wireless sensing tasks. There are however problems with labor-intensive training that involves gathering training samples and retraining efforts for trained systems. In order to complete wireless sensing tasks with fewer training efforts, [PERSON] and [PERSON] et al. [69] concentrated on the viability of utilizing deep learning networks.