The Ant Colony Optimization (ACO) algorithms have been well-studied by the Operations Research community for solving combinatorial optimization problems. A handful of researchers in the Data Science community have successfully implemented various ACO methodologies for rule-based classification. This family of ACO algorithms is referred to as AntMiner algorithms. Due to the flexibility of the framework, and the availability of alternative strategies at the modular level, a systematic review on the AntMiner algorithms can benefit the broader community of researchers and practitioners interested in highly interpretable classification techniques. In this paper, we provided a comprehensive review of each module of the AntMiner algorithms. Our motivation is to provide insight into the current practices and future research scope in the context of the rule-based classification. Our discussions address ACO methodologies, rule construction strategies, candidate selection metrics, rule quality evaluation functions, rule pruning strategies, methods to address continuous attributes, parameter selection, and experimental settings. This review also reports a summary of real-life implementations of the rule-based classifiers in diverse domains including medical, genetics, portfolio analysis, geographic information system (GIS), human-machine interaction (HMI), autonomous driving, ICT, quality, and reliability engineering. These implementations demonstrate the potential application domains that can be benefitted from the methodological contributions to the rule-based classification technique.

As additive manufacturing (AM) has become an evolving discipline in many industries, including manufacturing, medical, and aerospace, it becomes important to identify the risk coming from human exposure to particulates and Volatile Organic Compounds (VOCs) in AM which can lead to serious and chronic health issues. To address this issue, this article first provides a summary of previously reported particulate and VOCs characterization studies during AM processes, including equipment, environmental setups, variables studied, and instrumentation reported in the literature. We then reported a synopsis of the nature of the exposure, characteristics of the emitted particulates and VOCs, and associated health risks for different AM settings in a systematic manner. The key factors contributing to the harmful emissions include the use of toxic material compounds, high operating temperature, manual handling of hazardous materials, and limitations of the underlying printing technology among others. For that matter, we have outlined potential pathways to control hazardous exposure. Our recommendations include adopting safer operational practices, developing regulatory frameworks for facilities and equipment manufacturers, and seeking better technologies that minimize harmful emissions. Our aim is to benefit early-stage researchers, regulators, and industry practitioners in understanding and advancing knowledge of health hazards, safer practices, and technologies in AM.