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Mineral Identification Using Machine Learning Algorithms: A Data-Driven Approach
Subject area: Science,Engineering and Technology · Area of research: Bengaluru
DOI: https://doi.org/10.64388/IREV10I1-1719909
Abstract
The identification of minerals plays a significant role in geology, mining, environmental science, and industrial applications. Conventional mineral identification methods rely on laboratory testing and expert analysis, which are often time-consuming, expensive, and require specialized equipment. Recent advances in artificial intelligence and machine learning have introduced automated techniques capable of identifying minerals with high accuracy by analyzing their physical, chemical, and spectral characteristics. This paper presents a machine learning-based framework for mineral identification using supervised learning algorithms. The proposed approach involves data preprocessing, feature engineering, model training, performance evaluation, and prediction. Various algorithms, including Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN), are compared based on classification accuracy and computational efficiency. The study demonstrates that ensemble learning methods provide superior classification performance while maintaining robustness against noisy data. The proposed framework can assist geologists and mining industries in achieving faster and more reliable mineral classification.
Keywords
Mineral Identification, Machine Learning, Random Forest, Support Vector Machine, Artificial Intelligence, Classification, Data Mining
How to cite this paper
@article{1719909,
author = {Ghriti B. Amin },
title = {Mineral Identification Using Machine Learning Algorithms: A Data-Driven Approach},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {1},
pages = {1849-1853},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719909.pdf},
abstract = {The identification of minerals plays a significant role in geology, mining, environmental science, and industrial applications. Conventional mineral identification methods rely on laboratory testing and expert analysis, which are often time-consuming, expensive, and require specialized equipment. Recent advances in artificial intelligence and machine learning have introduced automated techniques capable of identifying minerals with high accuracy by analyzing their physical, chemical, and spectral characteristics. This paper presents a machine learning-based framework for mineral identification using supervised learning algorithms. The proposed approach involves data preprocessing, feature engineering, model training, performance evaluation, and prediction. Various algorithms, including Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN), are compared based on classification accuracy and computational efficiency. The study demonstrates that ensemble learning methods provide superior classification performance while maintaining robustness against noisy data. The proposed framework can assist geologists and mining industries in achieving faster and more reliable mineral classification.},
keywords = {Mineral Identification, Machine Learning, Random Forest, Support Vector Machine, Artificial Intelligence, Classification, Data Mining},
month = {July},
doi = {https://doi.org/10.64388/IREV10I1-1719909}
}