## 4 Deep Learning
The mainstream deep neural models are the deep belief network (DBN), a convolutional neural network (CNN), autoencoder (AE), recurrent NN (RNN) and the long short-term memory network (LSTMN). In the following, these architectures will be treated, referring to some specific applications, focusing attention on the input data and the types of sensors adopted for datasets creation. Among the most commonly used deep learning model in recognition and detection tasks is the CNN. In order to extract features which are resistant to distortion, CNNs use interconnected network architectures. A convolutional neural network (CNN) is a deep learning method that can use images as input, assign weight/importance to objects in the images and classify them. For simple application, a 1D convolutional neural network may be used. More sophisticated classification models, CNN-Net, Encoded-Net, and CNN-LSTM, will have more complicated architectures such as denser layers and larger kernel size than 1D CNN. Medical care benefits from automatic prediction of routine human activity. For the purpose of recognizing human activity, [PERSON] et al. [59] created the EnsemConvNet ensemble, which combines CNN-Net, Encoded-Net, and CNN-LSTM classification models. Each model can accept time-series data as a 2D matrix, and the EnsemConvNet model's classification result is created by combining different classifiers using techniques including majority voting, sum rules, product rules, and score fusion approaches. The suggested EnsemConvNet model outperforms the following deep learning models, according to the evidence: long short-term memory, multi-headed CNN, and CNN hybrid models. Input data are structured in multiple arrays, which may have different dimensions, depending on the signal type (language-based signals and sequences are 1D; 2D pictures or audio spectrograms; and 3D video or volumetric images.). CNN is a feedforward network, formed in the early stages by a sequence of convolution, pooling, non-linear activation layers and in the final stages by fully connected layers. In convolutional layers, a filtering operation is performed through a feature map in which units are organized, thus, getting a discrete convolution in order to identify local confluences of features from the preceding layer; hence the name given to these levels and more generally to the deep model. Simple features such as texture, lines, and edges are often extracted by the bottom convolution layers, whereas more abstract features are typically extracted by the top layer. ([PERSON] and [PERSON] et al. [60]). Pooling layers combine semantically related features into a single feature, resulting in more robust feature descriptions as well as down-sampling and dimensionality reduction processing. These operations include max-pooling, average-pooling, L2-pooling, and local contrast normalization. To improve CNN's capacity to fit non-linear data, activation layers' units perform non-linear procedures such as rectified linear unit (ReLU) or sigmoid units. Fully connected layers are located at the outermost level, closest to the output, and serve the purpose of classification. As in BPNNs, the back-propagation algorithm is employed for weights update. Numerous articles discuss the usage of CNN in numerous disciplines, and a significant amount of these works emphasize the importance of the sensor selection and sensor-related concerns. For images, recognition cameras are adopted. The widespread use of cell phones equipped with high resolution cameras makes available a huge amount of data that can be used for various applications. The combination of mobile phones and deep learning is a promising solution in many fields. In a framework for indoor localization, [PERSON] et al. [61] presented a deep learning-based convolutional neural network (CNN) localization based on smartphone photos. CNN is used to distinguish between floors, recognize inside scenes in a variety of lighting conditions, and improve indoor localization precision.