Following the trend of Industry 4.0, automation in different manufacturing processes has triggered the use of intelligent condition monitoring systems, which are crucial for improving productivity and availability of production systems. To develop such an intelligent system, semantic technologies are of paramount importance. This paper introduces an ontology that will be used to develop an intelligent condition monitoring system. The proposed ontology formalizes domain knowledge related to condition monitoring tasks of manufacturing processes. After introducing the ontology in detail, we evaluate the proposed ontology by instantiating it with a case study: a conditional maintenance task of bearings in rotating machinery.

As today’s manufacturing domain is becoming more and more knowledge-intensive, knowledge-based systems (KBS) are widely applied in the predictive maintenance domain to detect and predict anomalies in machines and machine components. Within a KBS, decision rules are a comprehensive and interpretable tool for classification and knowledge discovery from data. However, when the decision rules incorporated in a KBS are extracted from heterogeneous sources, they may suffer from several rule quality issues, which weakens the performance of a KBS. To address this issue, in this paper, we propose a rule base refinement approach with considering rule quality measures. The proposed approach is based on a rule integration method for integrating the expert rules and the rules obtained from data mining. Within the integration process, rule accuracy, coverage, redundancy, conflict, and subsumption are the quality measures that we use to refine the rule base. A case study on a real-world data set shows the approach in detail.

This article presents an hybrid and hierarchical model in which two modeling and simulation approaches, discrete event system specification simulation (DEVS) and semantic technologies, were used together in order to help in the analysis of a major healthcare problem, the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2). Indeed, the complexity of the SARS-CoV-2 replication process, and the range of hierarchical scales over which it interacts with cellular components (extending from genomic and transcriptomic to proteomic and metabolomic scales), and the intricate way in which they are interwoven, make its understanding very challenging. It is therefore crucial to model the different scales of the replication process, by taking into account all interactions with the infected cell. By combining the advantages of both DEVS simulation and ontological modeling, we propose a hierarchical ontology-based DEVS simulation model of the SARS-CoV-2 viral replication at both the micro-molecular (proteomic and metabolomic) and macro-molecular (genomic and transcriptomic) scales. First, we demonstrate the usefulness of combining DEVS simulation and semantic technologies in a common modeling framework to face the complexity of the SARS-CoV-2 viral replication at different scales. Second, the modeling and simulation of the SARS-CoV-2 replication process on different levels provide valuable information on the different stages of the virus’s life cycle and lays the foundation for a system to anticipate future mutations selected by the virus.

Medical diagnosis of cancer is becoming more complex in recent years, with doctors focusing on making both earlier and accurate diagnoses to save patients’ lives. Such goals are more challenging for melanoma, which is the deadliest of skin cancers. Recently, melanoma incidence has increased significantly because of climate change. Fortunately, early detection leads to a 5-year survival rate of 98 %. Computer-aided diagnosis systems can offer a more objective analysis tool, taking into consideration the expert’s knowledge. Ontology offers an efficient framework for reducing the gap between low-level information and expert analysis. A dermatologist’s recommendation is often based on the ABCD rule, involving four characteristics of a lesion, which are asymmetry, border, color, and differential structures. A score, associated to a qualitative description of the lesion, allows lesions to be categorized into three classes: melanoma, benign, or recommended follow-up. Early research on automatic diagnosis doesn’t take into consideration either the qualitative aspect of expert description or the ambiguity of information itself. In this paper, we propose a fuzzy ontology–based melanoma diagnosis system. A fuzzy classifier is proposed to cope with the qualitative description of experts. Then, a fuzzy inference system is proposed to generate the decision. Experimental validation is undertaken on both optical and dermoscopic images from public datasets DermQuest, Dermatology Information System, and International Skin Imaging Collaboration (ISIC). For optical images, we get a sensitivity of 91 %, a specificity of 88 %, and an accuracy of 90 %, whereas for dermoscopic images, we obtain a sensitivity of 92 % and 91 %, a specificity of 91 % and 93 %, and an accuracy of 91 % and 92 % for ISIC 2016 and ISIC 2017, respectively. A comparative study with existing approaches shows that these performances ensure higher accuracy rates and the best compromise between sensitivity and specificity.