# ML-Based Maintenance and Control Process Analysis, Simulation, and Automation--A Review
## 4 Discussion
### Directions for Further Research  
Further research into the analysis, simulation, and automation of ML-based maintenance and inspection processes should focus on improving data quality and availability, as high-quality data sets are essential for developing accurate and reliable ML models. Developing more interpretable models is another key direction, and efforts are aimed at making ML processes more transparent and understandable to users, thereby increasing trust and adoption. Researchers should also explore ways to make ML models more adaptable in dynamic environments, ensuring that they can respond quickly to changes without extensive retraining. Another important area is the integration of ML with other emerging technologies, such as edge computing and digital twins, to create more reliable and real-time maintenance and inspection systems. Addressing the problem of overfittingby developing methods that allow models to better generalise to new data is also critical. There is a need for research to reduce the complexity and cost of implementing ML-based systems, making them more accessible to small and medium-sized enterprises. Improving the cybersecurity of these systems is essential to protect against potential threats and ensure the integrity of operations. Further research should also focus on mitigating biases in ML models, ensuring fair and unbiased decision-making in industrial processes [57; 58; 59; 60; 61; 62]. The ethical implications of critical process automation should be an important focus of research, with the goal of establishing clear guidelines and best practices. This research should continue to explore the regulatory and compliance aspects of ML-based automation, helping to shape policies that balance innovation with security and accountability. It is noteworthy that much of the aforementioned research is interdisciplinary, requiring collaboration across disciplines and the use of multiple combined research methodologies. The study can be extended by acquiring more samples and also by expanding the database based on signals correlated with other types of traffic. Consideration should be given to whether it would be better to reduce the time window of the signal analysed or to use an adaptive input data width [63; 64]. A suitable match to the computational capabilities of computers is also an obstacle, as the signal presented in the study consists of 12,666 groups of 321 samples per 64 channels, resulting in 260,210,304 numerical values. In this case, it is possible to dispense with some of the electrodes and use those channels that correspond to the area of interest, such as the frontal lobe [65; 66; 67; 68; 69]. More broadly, further research into the classification of EEG signals using machine learning carries the potential to deepen our understanding of brain function and develop innovative applications. The most promising directions for future research include the following:  
* Artefact and noise robustness: improving EEG classification models that are robust to noise and artefacts can automatically detect and mitigate different types of artefacts in EEG signals, improving the reliability and interpretability of classification results. * Personalised and adaptive models: accounting for individual differences in brain activity patterns is essential to develop personalised EEG classification models with high accuracy and generalisation performance; hence, research into adaptive learning techniques and personalised model architectures will increase their utility and effectiveness in real-world applications. * Real-time classification systems: these are applicable to BCI, neurofeedback training, and assistive technologies, based on efficient algorithms and hardware implementations for real-time processing and classification of the EEG signal, enabling seamless integration with controlled and communication systems.