Volume 16 (2025)
Volume 15 (2024)
Volume 14 (2023)
Volume 13 (2022)
Volume 12 (2021)
Volume 11 (2020)
Volume 10 (2019)
Volume 9 (2018)
Volume 8 (2017)
Volume 7 (2016)
Volume 6 (2015)
Volume 5 (2014)
Volume 4 (2013)
Volume 3 (2012)
Volume 2 (2011)
Volume 1 (2010)
Exploration
Data mining in gravity field by utilizing clustering by self-organizing maps (case study in the southern part of Iran)

reza Shahnavehsi; Farnusch Hajizadeh

Articles in Press, Accepted Manuscript, Available Online from 25 February 2025

https://doi.org/10.22044/jme.2025.14879.2830

Abstract
  The present work is mainly about a method for illustrating the relation between the raw data in the same time; clustering is a key procedure to solve the problem of data division; also illustrating the connection among the elements of the research area simultaneously is important. Therefore, we propose ...  Read More

Exploration
Localizing the base learner weights in ensemble methods to improve the grade modeling accuracy

Ahmadreza Erfan; Saeed Soltani Mohammad; Maliheh Abbaszadeh

Articles in Press, Accepted Manuscript, Available Online from 09 December 2024

https://doi.org/10.22044/jme.2024.15242.2918

Abstract
  Machine learning (ML) has significantly transformed multiple disciplines, including mineral resource evaluation in mining engineering, by facilitating more accurate and efficient estimation methods.  Ensemble methods, as a fundamental component of modern machine learning, have emerged as powerful ...  Read More

Exploration
Machine Learning Predictive Approaches for Cu-Au Mineral prospectivity Map in Sonajil, NW of Iran: an Improvement by a Bayesian Semi-supervised Algorithm

Mohammadjafar Mohammadzadeh; Majid Mahboubiaghdam; Moharram Jahangiri; Aynur Nasseri

Volume 14, Issue 4 , October 2023, , Pages 1321-1342

https://doi.org/10.22044/jme.2023.13313.2445

Abstract
  Most machine learning-monitored algorithms used to create mineral potential prediction maps require noise-free data to achieve high performance and reliable results. Unsupervised clustering methods are highly effective for uncovering a dataset’s hidden structures. Therefore, this study attempts ...  Read More