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Communication Dans Un Congrès Année : 2020

Chemical Laboratories 4.0: A Two-Stage Machine Learning System for Predicting the Arrival of Samples

Résumé

This paper presents a two-stage Machine Learning (ML) model to predict the arrival time of In-Process Control (IPC) samples at the quality testing laboratories of a chemical company. The model was developed using three iterations of the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology, each focusing on a different regression approach. To reduce the ML analyst effort, an Automated Machine Learning (AutoML) was adopted during the modeling stage of CRISP-DM. The AutoML was set to select the best among six distinct state-of-the-art regression algorithms. Using recent real-world data, the three main regression approaches were compared, showing that the proposed two-stage ML model is competitive and provides interesting predictions to support the laboratory management decisions (e.g., preparation of testing instruments). In particular, the proposed method can accurately predict 70% of the examples under a tolerance of 4 time units.
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hal-04060673 , version 1 (06-04-2023)

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António João Silva, Paulo Cortez, André Pilastri. Chemical Laboratories 4.0: A Two-Stage Machine Learning System for Predicting the Arrival of Samples. 16th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2020, Neos Marmaras, Greece. pp.232-243, ⟨10.1007/978-3-030-49186-4_20⟩. ⟨hal-04060673⟩
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