Upon a summarization of related works, a specific case study is conducted on modeling a textile finishing process using artificial neural networks (ANN), integrating analytic hierarchy process (AHP) and reinforcement learning (RL) algorithm, to develop a novel multi-criteria decision support system. The model can map the uncertain interrelationship of the parameters and performances of a textile manufacturing process that lay the foundation of a decision support system. In order to overcome these issues and provide a feasible solution to textile manufacturing firms, machine learning techniques are proposed to construct decision support systems. Whereas the intricate relationship between the large-scale variables from a variety of textile processes can lead to the extreme difficulty of decision-making issues. In the Industry 4.0 era, the textile manufacturing process is expected to be more flexible. The textile industry contributes significantly to the world economy. Finally, the conclusions, as well as perspectives on future research directions, will be discussed in the final chapter of this Habitation thesis.
Next, an electrocardiogram (ECG) signal monitoring system with applications in smart healthcare systems using explainable federated learning techniques will be introduced. Fourth, research on network security with anomaly detection techniques will be detailed with applications in the fields of finance, wireless sensor networks, as well as industrial control systems. Third, the contributions of forecasting and anomaly detection using machine learning techniques with applications in the field of supply chain management will be examined with data from the fashion industry. Second, contributions in the field of modeling and optimizing production using reinforcement learning with a case study in textile manufacturing will be introduced. First, we will introduce contributions in the field of statistical process monitoring, specifically advanced control charts developed based on statistical techniques as well as machine learning. This thesis will systematically present contributions in Monitoring, Anomaly Detection, and Optimization for Industrial Systems with Statistical and Machine Learning techniques. The recent development of information and communication technologies such as the IoT, and Artificial Intelligence (AI) to drive continuous improvement, knowledge transfer, and data-driven decision-making in many fields. Nowadays, industrial systems, such as production systems, industrial control systems, wireless communication networks, robotic systems, and healthcare systems, are becoming complex and more and more connected to the Internet with the Internet of things (IoT) technology.
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