A neural network approach for classification of fault-slip data in geoscience-Reference-Cited by-同舟云学术

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A neural network approach for classification of fault-slip data in geoscience

Published:2024-01 Issue:1 Volume:15 Page:102325
ISSN:2090-4479
Container-title:Ain Shams Engineering Journal
language:en
Short-container-title:Ain Shams Engineering Journal

Author:

Yaman Sertaç^ORCID,Karakaya Barış,Köküm Mehmet^ORCID

Publisher

Elsevier BV

Subject

General Engineering

Reference32 articles.

1. Machine learning in geosciences and remote sensing;Lary;Geosci Front,2016

2. Machine learning for the geosciences: challenges and opportunities;Karpatne;IEEE Trans Knowl Data Eng,2018

3. Mousavi SM, Beroza GC. A machine-learning approach for earthquake magnitude estimation. Geophys Res Lett 2020;47(1):e2019GL085976.

4. Landslide prediction from machine learning;Korup;Geol Today,2014

5. Tectonic Analysis of the Slip Data Sets;Jacques;J Geophys Res,1984

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1. Kinematic and dynamic fault slip analyses: Implications from the surface rupture of the 2023 Elbistan (Kahramanmaraş) (Mw7.6) earthquake, Türkiye;Journal of Structural Geology;2024-10

2. Optimizing agricultural data security: harnessing IoT and AI with Latency Aware Accuracy Index (LAAI);PeerJ Computer Science;2024-08-28

3. Kar Erime Optimizasyonu Algoritması ile Çok Katmanlı Yapay Sinir Ağının Eğitimi;Çukurova Üniversitesi Mühendislik Fakültesi Dergisi;2024-07-11

4. Wind Tunnel Data Classification A Comparison of Performance Between K-Nearest Neighbor and Artificial Neural Network Algorithm;2023 10th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI);2023-09-20

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