# ML-Based Maintenance and Control Process Analysis, Simulation, and Automation--A Review
## 1 Introduction
Networks that examine the raw signal associated with specific human movements or images can help to develop hands-free tools without the need to calibrate the device for each user. Processing the signal to find similar features using deep neural networks can lead to the development of cloud-structured systems or the ability to use devices in a plug-and-play manner. With the wide range of networks available, they can adapt to the individual and be pre-trained with a specific architecture tailored to the hardware. Notably, a pre-built and pre-trained network does not consume much computing power during data classification. Neural networks have high classification capabilities, and deep learning allows more accurate signal features to be extracted. A characteristic of deep learning networks is an arbitrary number and distribution of so-called hidden layers, in which the associated neurons operate in layers, resulting in more accurate data classifications or a larger data  
Figure 4: Results of the review of publications with the keywords ‘EEG classification’ and related terms. Figure 5: Results of the review of publications with the keywords ‘EEG classification’, ‘AI’, and related terms. Figure 6: Results of the review of publications with the keywords ‘EEG classification’, ‘ML’, and related. group for a given feature. Some networks have continuous learning capabilities, such as recurrent networks or reinforcement learning networks, such that they can continuously evolve in a system when provided with appropriate rewards for correct performance. Deep neural networks, also known as deep networks, are distinguished by their ability to have multiple hidden layers. These layers can detect increasingly complex features in the data, which can lead to better classification and prediction performance. In deep networks, the learning process takes place by iteratively transforming the input data into increasingly abstract representations, allowing the network to learn complex patterns in that data. Recurrent networks are a type of deep network with the unique ability to process data sequences and continuously learn. Due to their recursive structure, these networks can analyse sequential data, such as text or time series, and remember past contexts, which is crucial for understanding data temporally. In addition, reinforcement learning networks have learning mechanisms that allow them to dynamically adapt their behaviour based on responses and rewards from the environment, enabling them to continuously improve their performance. In this way, deep networks, including recurrent networks and reinforcement learning networks, are powerful tools in the field of artificial intelligence for adaptive and efficient data processing and decision-making in dynamically changing environments. Convolutional (braided) networks are another network model used in signal analysis. They are characterised by using data input for analysis and are divided into two-dimensional and one-dimensional types. Two-dimensional networks are most commonly used to analyse data folded into matrices, after which, characteristics are extracted within a specified window (e.g., 2 \(\times\) 2), followed by further processing. The window is shifted by a certain value depending on its size, and the characteristics are extracted to a lower layer. This operation method can be extended to a network after this extraction, where the data would have to be flattened. One-dimensional spliced networks work similarly, but the window has only one dimension. However, regardless of the network type, the channels responsible for distributing colours (red, green, and blue) during image classification must also be determined. Research is being conducted in the field of biomedical signal analysis based on convolutional neural networks and deep learning networks. This research is based on a database from MIT-BIH that is used to create a better-optimised alternative to existing approaches using EEG-like signals such as the ECG (electrocardiograph) [6].