Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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Session Overview |
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Regular Session : Classification and data management methods
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Presentations | ||
ID: 198
/ RS-08: 1
Regular Paper Submission Data-driven Production Management: Machine Learning and Artificial Intelligence Keywords: Sawmill simulator metamodels, Artificial Intelligence, Iterative Closest Point dissimilarity Dissimilarity to class medoids as features for 3D point cloud classification CRAN, France ID: 278
/ RS-08: 2
Regular Paper Submission Data-driven Production Management: Machine Learning and Artificial Intelligence Keywords: Agglomerative Hierarchical clustering, BIRCH clustering, COVID-19, K-means clustering, P-median A Comparative Study of Classification Methods on the States of the USA based on COVID-19 Indicators 1Gaziantep University, Turkey; 2Gaziantep University, Turkey ID: 334
/ RS-08: 3
Regular Paper Submission Data-driven Production Management: Machine Learning and Artificial Intelligence Smart Manufacturing & Industry 4.0: Intelligent Maintenance Systems Keywords: Condition-Based Maintenance (CBM), Maintenance data, Data management Maintenance data management for condition-based maintenance implementation ALGORITMI Research Centre, University of Minho, Guimarães, Portugal ID: 392
/ RS-08: 4
Regular Paper Submission Data-driven Production Management: Machine Learning and Artificial Intelligence Smart Manufacturing & Industry 4.0: Intelligent Maintenance Systems Keywords: Smart maintenance, Predictive maintenance, Machine learning, Health assessment, Feature selection and fusion A machine learning based health indicator construction in implementing predictive maintenance: A real world industrial application from manufacturing Chalmers University of Technology, Sweden ID: 571
/ RS-08: 5
Regular Paper Submission Keywords: Artificial Intelligence, Machine Learning, aviation security, X-ray detection Development of Convolutional Neural Network Architecture for Detecting Dangerous Goods for X-ray Aviation Security in Artificial Intelligence 1Department of Industrial Management Engineering, Korea University, Korea, Republic of (South Korea); 2School of Industrial Management Engineering, Korea University, Korea, Republic of (South Korea) |