Fast anomaly detection with locality-sensitive hashing and hyperparameter autotuning-Reference-Cited by-同舟云学术

Fast anomaly detection with locality-sensitive hashing and hyperparameter autotuning

Published:2022-08 Issue: Volume:607 Page:1245-1264
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Meira Jorge,Eiras-Franco Carlos,Bolón-Canedo Verónica,Marreiros Goreti,Alonso-Betanzos Amparo

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference49 articles.

1. Application of adaptive reliability importance sampling-based extended domain PSO on single mode failure in reliability engineering;Bai;Information Sciences,2021

2. Deep learning for diagnosis and classification of faults in industrial rotating machinery;Souza;Computers & Industrial Engineering,2021

3. A novel IoT network intrusion detection approach based on adaptive particle swarm optimization convolutional neural network;Kan;Information Sciences,2021

4. Transfer learning based intrusion detection scheme for Internet of vehicles;Li;Information Sciences,2021

5. Combining unsupervised and supervised learning in credit card fraud detection;Carcillo;Information sciences,2021

Cited by 9 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. Outlier detection using conditional information entropy and rough set theory;Journal of Intelligent & Fuzzy Systems;2024-01-10

2. A new unsupervised outlier detection method;Journal of Intelligent & Fuzzy Systems;2024-01-10

3. AT-densenet with salp swarm optimization for outlier prediction;International Journal of Computers and Applications;2023-10-26

4. Intracranial Haemorrhage Detection Based on Deep Learning Using CT Images;Proceedings of the 2023 Fifteenth International Conference on Contemporary Computing;2023-08-03

5. Artificial intelligence-driven malware detection framework for internet of things environment;PeerJ Computer Science;2023-05-29