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Prediction and classification of minerals using deep residual neural network

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Abstract

Minerals are in great demand because of their pervasive application in atomic energy and their use as raw materials for other industries. Despite this, it is challenging to identify and classify the minerals due to the deposit's broadness, mineral composition, particle size, and geography. In the proposed study, a deep computer vision technology supported by a deep residual neural network model is developed to establish a system for classifying and identifying minerals. Convolutional feature selection is used in this model to apply the filters and extract the mineral characteristics from the mineral images. This model offers a better and more effective model while minimizing reliance on mineral images with high resolution. Additionally, VGG with a depth of 16 and residual neural networks with depths of 101, 34, and 18 are developed by combining various pooling algorithms for mineral identification. The proposed model's performance has been thoroughly investigated and evaluated using the confusion matrix, sensitivity, classification accuracy, and specificity scores. The proposed model classified numerous minerals with an accuracy of 91%.

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Data availability

Data are available on request from the authors.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, GITAM School of Technology, GITAM University Bengaluru, Bengaluru, India

    Prasannavenkatesan Theerthagiri

  2. School of Computing Science and Engineering, VIT Bhopal University, Bhopal, India

    A. Usha Ruby & J. George Chellin Chandran

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  1. Prasannavenkatesan Theerthagiri
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  2. A. Usha Ruby
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  3. J. George Chellin Chandran
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Contributions

PT helped in conceptualization, methodology, and software. AUR helped in data curation and writing—original draft preparation. GCCJ worked in software, validation, and writing—reviewing and editing.

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Correspondence to Prasannavenkatesan Theerthagiri.

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Theerthagiri, P., Ruby, A.U. & George Chellin Chandran, J. Prediction and classification of minerals using deep residual neural network. Neural Comput & Applic 36, 1539–1551 (2024). https://doi.org/10.1007/s00521-023-09141-4

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  • DOI: https://doi.org/10.1007/s00521-023-09141-4

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