首页|Investigators at University of Ghent Describe Findings in Machine Learning (Expl ainable Real-time Predictive Analytics On Employee Workload In Digital Railway C ontrol Rooms)
Investigators at University of Ghent Describe Findings in Machine Learning (Expl ainable Real-time Predictive Analytics On Employee Workload In Digital Railway C ontrol Rooms)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Machine Learning are discussed in a new report. According to news reporting out of Ghent, Belgium , by NewsRx editors, research stated, “Both workload peaks and lows contribute t o lower employee well-being. Predictive employee workload analytics can empower management to undertake proactive prevention.” Funders for this research include VT (Virginia Tech) -INFORMS Student Chapter, A gence nationale pour le developpement de la recherche en sante (ANDRS). Our news journalists obtained a quote from the research from the University of G hent, “For this purpose, we develop a real-time machine learning framework to pr edict and explain future workload in a challenging environment with variable imb alanced workload: the digital control rooms for railway traffic management of In frabel, Belgium’s railway infrastructure company. The proposed two-stage methodo logy leverages granular data of workload categories that are very different in n ature and separates the effects of workload presence and magnitude. In this way, set-up addresses the changing workload mix over 15-minute intervals. We extensi vely benchmark machine learning and deep learning models within this context, le ading to LightGBM (Light Gradient Boosting Machine) as the best-performing model . SHAP (SHapley Additive exPlanations) values highlight the benefits of disentan gling presence and magnitude and reveal associations with human-machine interact ion and team exposure. As a proof of concept, our implemented predictive model o ffers tailored decision support to the traffic supervisor in an explainable way. ”
GhentBelgiumEuropeCyborgsEmergin g TechnologiesMachine LearningPredictive AnalyticsUniversity of Ghent
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