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

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Chemical Laboratories 4.0: A Two-Stage Machine Learning System for Predicting the Arrival of Samples
Creator	Cortez, Paulo Pilastri, André Silva, António
Source	PMC
abstract	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.
has issue date	2020-05-06(xsd:dateTime)
bibo:doi	10.1007/978-3-030-49186-4_20
has license	no-cc
sha1sum (hex)	3f816a1cbb4ad735a54792bf22fb55fe51b849c2
schema:url	https://doi.org/10.1007/978-3-030-49186-4_20
resource representing a document's title	Chemical Laboratories 4.0: A Two-Stage Machine Learning System for Predicting the Arrival of Samples
has PubMed Central identifier	PMC7256590
schema:publication	Artificial Intelligence Applications and Innovations
resource representing a document's body	covid:3f816a1cbb4ad735a54792bf22fb55fe51b849c2#body_text
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