Anomaly detection in time-series data using evolutionary neural architecture search with non-differentiable functions-Reference-Cited by-同舟云学术

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Anomaly detection in time-series data using evolutionary neural architecture search with non-differentiable functions

Published:2024-04 Issue: Volume:155 Page:111442
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Gomez-Rosero Santiago^ORCID,Capretz Miriam A.M.^ORCID

Funder

Natural Sciences and Engineering Research Council of Canada

Ontario Ministry of Colleges and Universities

Ministry of Training, Colleges and Universities

Secretaría de Educación Superior, Ciencia, Tecnología e Innovación

Publisher

Elsevier BV

Reference59 articles.

1. Deep learning applications and challenges in big data analytics;Najafabadi;J. Big Data,2015

2. Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies;Xie;J. Biomed. Inform.,2022

3. Deep cybersecurity: A comprehensive overview from neural network and deep learning perspective;Sarker;SN Comput. Sci.,2021

4. Deep learning models for predictive maintenance: A survey, comparison, challenges and prospects;Serradilla;Appl. Intell.,2022

5. On the complexity of neural network classifiers: A comparison between shallow and deep architectures;Bianchini;IEEE Trans. Neural Netw. Learn. Syst.,2014

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