Welding is an indispensable manufacturing process widely used across different commercial industries. Embedded monitoring and control systems are necessary to make this process more maintainable, error-free, and cost-effective. This paper reviews literature about machine learning approaches for improving embedded quality monitoring and control systems for welding processes. Based on the literature included in international peer-reviewed journals and conferences, we build a comprehensive overview of pre-process, in-process, and post-process approaches for established welding techniques. Furthermore, we motivate future research for technologies that enable detecting defects in the manufacturing environment.

Big Data has the potential to improve demand forecasting methods and detect supply chain disruptions. This paper reviews literature about machine learning tools for supporting supply chain management from a manufacturing perspective. Based on the literature included in international peer-reviewed journals and conferences, we build a task-oriented, comprehensive overview for the manufacturing domain according to the supply chain management operations reference model. Furthermore, we identify future research needs. Our findings aim to foster the understanding, acceptance, and adaption of these new machine learning tools to support people managing the supply chain.

In this paper, a literature review of recent advances in machine learning approaches for cyber defense is presented. Relevant articles in the databases ACM DL, IEEE Xplore DL, and ScienceDirect were identified and supplemented by forward and backward searches. In total, 70 articles were identified to meet the scope of the literature review. The following article gives an overview of classifications, datasets, and algorithms of machine learning in cyber defense. Limitations and future research areas are identified.

The consequences of alcohol use disorders affect around 17 million people in the United States. To prevent healthy people from developing an alcohol use disorder, early detection is achieved using different screening methods. However, these methods are mostly based on self-tests, which can be easily influenced by the subject. In order to prevent healthy people from developing an alcohol use disorder through their alcohol consumption, drinking behavior, and alcohol-related problems, we propose a novel machine learning approach. With this approach it is possible to classify healthy people with an accuracy of 69 percent based on EEG recordings in assessing the danger of developing an alcohol use disorder or not. To obtain this result, the frequency range of the EEG data used was divided into 99 fine bands. Using a machine learning algorithm, the five most important bands were identified.

Growing data acquisition in smart grid technology leads to considerable privacy concerns, requiring standardized and sophisticated solutions that ensure consumers’ privacy. In this paper, we systematically review international peer-reviewed publications, targeting to improve and ensure data privacy in the context of smart meter data analysis. Our research shows that a remarkable trend towards privacy-preserving aggregation schemes has been established in recent years, advancing future research and providing data privacy for smart grid participants.