# Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System
## 4 Emerging Technologies
### Intelligence
#### 4.3.3 Deep Learning (DL)  
Deep Learning (DL) is a new advanced application of AI which improves ML algorithms, offering multiple advantages in many fields. DL is an integrated to ML and AI technology, which has application in the last 10 years. DL creates powerful data models and the proven success of these techniques in many public tenders has helped to significantly increase ML applications in pharmaceutical companies over the last 2 years [52]. The DL uses intelligent, multilayer Deep Neural Networks (DNN) to create systems that can perform detection characteristics of massive amounts of training data without label. DL can have a huge number of hidden levels because it uses more powerful CPU and GPU hardware, while conventional neural networks typically use one or two hidden layers because there is hardware constraint.  
Neural network technology was developed to solve many difficult tasks, including many medical problems, such as B. formula. DNN models can more accurately diagnose diseases or disorders because they are very effective with severe class imbalances. A typical DNN includes a prediction module for predicting disease likelihood and an interpretation module for identifying risk factors for disease progression to identify whether patients at high risk of developing a disease are exposed Disease in severe disease. In addition, DNNs can be used to predict early recurrence of a patient's condition or disease [53].  
In a neural network (NN), the input features are fed to the input layer, and projections are generated from the output layer after a series of nonlinear transformations using hidden layers [54]. This usually happens because back-propagated errors are used to gradually reduce the difference between expected and received output values. Each node to be predicted corresponds to an output with a class. When only one node in the network is at egress level, the network is called NN operating system [29].  
In the branch of medicine, DL offers the ability to analyze data at exceptional speeds with efficiency and accuracy. Moreover, DL can solve complex problems. Thus, DL can be used in order to reduce the admin load and increase insights into patient care and requirements, as it provides an improved analysis of medical images and data [55]. Furthermore, DL algorithms are widely utilized in biometrics. Behavioral biometrics such as gait, keystroke, or electrocardiogram have been researched through DL approaches. Biometrics is a field heavily related to patterns, while DL tools have offered remarkable performances in pattern recognition. Therefore, it has been highly affected by these sophisticated algorithms because data classification implemented within a DL network avoids complex and time-consuming signal transformations. Thus, the capability of identifying patterns in time-inconsistent signals, such as biometric ones, via DL algorithms has broadened the potentials of enabling and advancing accurate medical diagnosis and treatment [56]. The evolution of AI, ML, and DL is depicted in Figure 6.