Obsolescence Prediction based on Joint Feature Selection and Machine Learning Techniques-Reference-Cited by-同舟云学术

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Obsolescence Prediction based on Joint Feature Selection and Machine Learning Techniques

Published:2021 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 13th International Conference on Agents and Artificial Intelligence
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Author:

Trabelsi Imen¹²,Zeddini Besma³,Zolghadri Marc¹⁴,Barkallah Maher²,Haddar Mohamed²

Affiliation:

1. Quartz Laboratory, SUPMECA, 3 rue Fernand Hainaut, 93407 Saint-Ouen, France, --- Select a Country ---

2. LA2MP Laboratory, ENIS, Route Soukra Km 3.5, 3038 Sfax, Tunisia, --- Select a Country ---

3. SATIE Laboratory CNRS, UMR 8029, CYTeh ENS Paris-Saclay, Cergy, France, --- Select a Country ---

4. LAAS - CNRS, 7 Avenue du Colonel Roche, 31400 Toulouse, France, --- Select a Country ---

Publisher

SCITEPRESS - Science and Technology Publications

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Obsolescence in Operating Systems and Microprocessors;2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA);2023-05-23

2. Forecasting Obsolescence of Components by Using a Clustering-Based Hybrid Machine-Learning Algorithm;Sensors;2022-04-23

3. A New Approach to Evaluate and Predict System Obsolescence: Mathematical Formulation;Lecture Notes in Mechanical Engineering;2021-09-21

4. X-PHM: Prognostics and health management knowledge-based framework for SME;Procedia CIRP;2021

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