Comparison results of multi-classification.

<div><p>Industrial sensor networks exhibit heterogeneous, federated, large-scale, and intelligent characteristics due to the increasing number of Internet of Things (IoT) devices and different types of sensors. Efficient and accurate anomaly detection of sensor data is essential for guar...

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Bibliographic Details
Main Author:	Tao Yang (184280) (author)
Other Authors:	Xuefeng Jiang (1433992) (author), Wei Li (7081) (author), Peiyu Liu (5623034) (author), Jinming Wang (191356) (author), Weijie Hao (11466973) (author), Qiang Yang (135832) (author)
Published:	2025
Subjects:	Molecular Biology Developmental Biology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified system &# 8217 numerical results demonstrate intelligent characteristics due inserting long short implemented using gaussian heavy traffic load industrial sensor networks improving detection performance term memory network graph convolutional network sensor data generated industrial sensor data industrial sensor network accurate anomaly detection several inherent limitations proposed approach outperforms sensor data anomaly detection proposed approach time communication substantial volume paper develops packets lost operational reliability normal operation mainly consists latter based key role individual edges increasing number high requirements gcn ), full extraction extensively assessed existing state existing research effectively filter effectively extract different types control center comprehensively analyzing bayesian algorithms anomalous state
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Comparison results of multi-classification.

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