Sensor Anomaly Detection using Self Features Organizing Maps and Hierarchical-Clustring for Water Quality Assessment-Reference-Cited by-同舟云学术

Sensor Anomaly Detection using Self Features Organizing Maps and Hierarchical-Clustring for Water Quality Assessment

Published:2022-11-26 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference of Advanced Technology in Electronic and Electrical Engineering (ICATEEE)
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Author:

Khelil Mohamed Imed¹,Ladjal Mohamed²,Ouali Mohammed Assam²,Bennacer Hamza³

Affiliation:

1. University of M’Sila,LGE, Laboratoire de Génie Electrique,Department of Electrical Engineering Faculty of Technology,Algeria

2. University of M’Sila,LASS, Laboratoire d’Analyse des Signaux et Systèmes,Department of Electronics, Faculty of Technology,Algeria

3. Faculty of Technology, University of M'Sila,ECP3M Laboratory, University-of Mostaganem, Algeria,Electronics Department,Algeria

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10093233/10093088/10093763.pdf?arnumber=10093763

Reference18 articles.

1. Anomaly detection using self-organizing map and wavelets in wireless sensor networks;siripanadorn;2010 In Proceedings of the 10th WSEAS international conference on Applied computer science (ACS’10),2010

2. Self-Organizing Maps-Based Features Selection with Deep LSTM and SVM Classification Approaches for Advanced Water Quality Monitoring;khelil;Intelligent Systems Engineering J,2022

3. A patent quality analysis and classification system using self-organizing maps with support vector machine

4. Neuro-immune and self-organizing map approaches to anomaly detection: A comparison;gonzalez;2002 In First International Conference on Artificial Immune Systems (ICARIS),2002

5. Time Series Clustering for Anomaly Detection Using Competitive Neural Networks

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