首页|New Findings in Artificial Intelligence Described from Rennes School of Business (Explainable Artificial Intelligence In Transport Logistics: Risk Analysis for Road Accidents)
New Findings in Artificial Intelligence Described from Rennes School of Business (Explainable Artificial Intelligence In Transport Logistics: Risk Analysis for Road Accidents)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Artificial Intelligence. According to news reporting originating in Rennes, Fran ce, by NewsRx journalists, research stated, "Automobile traffic accidents repres ent a significant threat to global public safety, resulting in numerous injuries and fatalities annually. This paper introduces a comprehensive, explainable art ificial intelligence (XAI) artifact design, integrating accident data for utiliz ation by diverse stakeholders and decision-makers." The news reporters obtained a quote from the research from the Rennes School of Business, "It proposes responsible, explanatory, and interpretable models with a systems-level taxonomy categorizing aspects of driver-related behaviors associa ted with varying injury severity levels, thereby contributing theoretically to e xplainable analytics. In the initial phase, we employed various advanced techniq ues such as data missing at random (MAR) with Bayesian dynamic conditional imput ation for addressing missing records, synthetic minority oversampling technique for data imbalance issues, and categorical boosting (CatBoost) combined with SHa pley Additive exPlanations (SHAP) for determining and analyzing the importance a nd dependence of risk factors on injury severity. Additionally, exploratory feat ure analysis was conducted to uncover hidden spatiotemporal elements influencing traffic accidents and injury severity levels. We developed several predictive m odels in the second phase, including eXtreme Gradient Boosting (XGBoost), random forest (RF), deep neural networks (DNN), and fine-tuned parameters. Using the S HAP approach, we employed model-agnostic interpretation techniques to separate e xplanations from models. In the final phase, we provided an analysis and summary of the system-level taxonomy across feature categories."
RennesFranceEuropeArtificial Intel ligenceEmerging TechnologiesMachine LearningRisk and PreventionRennes Sc hool of Business
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