Impact of Extreme Class-Imbalance on Landslide-Risk Prediction and Mitigation Using Two-Stage Deep Neural Network-Reference-Cited by-同舟云学术

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Impact of Extreme Class-Imbalance on Landslide-Risk Prediction and Mitigation Using Two-Stage Deep Neural Network

Published:2022-11 Issue: Volume: Page:
ISSN:
Container-title:2022 14th International Conference on Signal Processing Systems (ICSPS)
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Author:

Tengtrairat N.¹,Woo W. L.²,Parathai P.²,Sundaranaga C.³,Na Ayutthaya T. Kridakorn³,Rinchumphu D.⁴

Affiliation:

1. School of Software Engineering Payap University,Chiang Mai,Thailand

2. Northumbria University,Department of Computer and Information Sciences,Newcastle upon Tyne,UK

3. Chiang Mai University,City Research and Development Center, Faculty of Engineering,Chiang Mai,Thailand

4. Chiang Mai University,Faculty of Engineering,Department of Civil Engineering,Chiang Mai,Thailand

Funder

Japan Society for the Promotion of Science (JSPS)

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10218342/10218346/10218725.pdf?arnumber=10218725

Reference24 articles.

1. Three-Dimensional Landslide Model For Predicting Affected Area With Particle Flow Simulation;nobaew;J Inf Sci Technol,2010

2. Noise tolerant drift detection method for data stream mining

3. SVMs Modeling for Highly Imbalanced Classification

4. Concept Drift Detection by Tracking Weighted Prediction Confidence of Incremental Learning

5. Single-Channel Blind Separation Using Pseudo-Stereo Mixture and Complex 2-D Histogram

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