Data-driven AHP: a novel method for porphyry copper prospectivity mapping in the Varzaghan District, NW Iran

Saremi, Mobin; Maghsoudi, Abbas; Hoseinzade, Zohre; Mokhtari, Ahmad Reza

doi:10.1007/s12145-024-01481-6

Earth Sci Inform

2024

DOI: 10.1007/s12145-024-01481-6

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Data-driven AHP: a novel method for porphyry copper prospectivity mapping in the Varzaghan District, NW Iran

Mobin Saremi,

Abbas Maghsoudi,

Zohre Hoseinzade

et al.

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Cited by 2 publications

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Quantitative assessment of information technology support in higher education management systems for student employability enhancement

2024

Applied Mathematics and Nonlinear Sciences

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This paper synthesises the definition of information technology, the definition of students’ employability, and the principle of constructing the evaluation index system, and initially formulate the evaluation system of information technology-driven employability improvement. To ensure the accuracy of the evaluation index system, we used the Delphi method to conduct two rounds of screening. Finally, the evaluation index system for this paper was determined. Then, the hierarchical analysis algorithm is used to calculate the weight value of each index, which is substituted into the fuzzy comprehensive evaluation model as the input value to complete the quantitative assessment of the enhancement of students’ employment ability under the support of information technology. The results show that the affiliation matrix obeys the principle of maximum affiliation, and the quantitative assessment result of information technology-driven students’ employability enhancement in a school is deduced to be excellent, with the specific data expressed as 4.53601, which indicates that information technology fuels students’ employability enhancement and enables them to adapt to the society and the workplace more quickly.

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Quantitative assessment of information technology support in higher education management systems for student employability enhancement

2024

Applied Mathematics and Nonlinear Sciences

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Geochemical Anomaly Detection and Pattern Recognition: A Combined Study of the Apriori Algorithm, Principal Component Analysis, and Spectral Clustering

Hajihosseinlou,

Maghsoudi,

Ghezelbash

2024

Minerals

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This study demonstrates the effectiveness of combining Principal Component Analysis (PCA) and the Apriori algorithm for feature selection, alongside Spectral clustering, to detect geochemical anomalies in Mississippi Valley-Type (MVT) Pb-Zn deposits in western Iran. First, PCA and Apriori enabled the identification of both syngenetic and epigenetic components, which helped in recognizing elements associated with mineralization. These elements were then modeled using Spectral clustering to detect geochemical anomalies. Unlike traditional methods like k-means, Spectral clustering does not require spherical clusters and is adept at identifying clusters of arbitrary shapes. This made it particularly suitable for analyzing the irregular shapes of geochemical anomalies in the study area. By incorporating Spectral clustering, the method effectively separated geochemical groups, revealing the underlying structure of the data. This was crucial for identifying anomalous geochemical zones and delineating areas with a high potential for Pb-Zn mineralization. The performance of the Spectral clustering algorithm was thoroughly evaluated using the Silhouette Score, the Davies–Bouldin Index, and Dunn Index. Subsampling was employed to assess the algorithm’s stability, providing a comprehensive evaluation of its effectiveness in identifying geochemical anomalies and mapping mineralization potential.

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Data-driven AHP: a novel method for porphyry copper prospectivity mapping in the Varzaghan District, NW Iran

Cited by 2 publications

References 46 publications

Quantitative assessment of information technology support in higher education management systems for student employability enhancement

Quantitative assessment of information technology support in higher education management systems for student employability enhancement

Geochemical Anomaly Detection and Pattern Recognition: A Combined Study of the Apriori Algorithm, Principal Component Analysis, and Spectral Clustering

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