Unsupervised Autoencoder-Based Feature Ranking and Anomaly Detection for Porphyry Copper Prospectivity Mapping from Multi-Source Geospatial Datasets.

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Title: Unsupervised Autoencoder-Based Feature Ranking and Anomaly Detection for Porphyry Copper Prospectivity Mapping from Multi-Source Geospatial Datasets.
Authors: Saremi, Mobin1 (AUTHOR), Hoseinzade, Zohre2 (AUTHOR), Shirazy, Adel1,3 (AUTHOR), Shirazi, Aref1,4 (AUTHOR), Beiranvand Pour, Amin4,5 (AUTHOR) beiranvand.pour@umt.edu.my
Source: Minerals (2075-163X). Jun2026, Vol. 16 Issue 6, p660. 24p.
Database: Academic Search Ultimate
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  Data: Unsupervised Autoencoder-Based Feature Ranking and Anomaly Detection for Porphyry Copper Prospectivity Mapping from Multi-Source Geospatial Datasets.
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=194915511
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        Value: 10.3390/min16060660
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      – Code: eng
        Text: English
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        PageCount: 24
        StartPage: 660
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      – TitleFull: Unsupervised Autoencoder-Based Feature Ranking and Anomaly Detection for Porphyry Copper Prospectivity Mapping from Multi-Source Geospatial Datasets.
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            NameFull: Hoseinzade, Zohre
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            NameFull: Shirazy, Adel
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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