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Optimizing mineral identification for sustainable resource extraction through hybrid deep learning enabled FinTech model

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  • Radulescu, Magdalena
  • Dalal, Surjeet
  • Lilhore, Umesh Kumar
  • Saimiya, Sarita

Abstract

Mineral extraction and use are vital to the world economy and industrial and energy industries. Environmental implications and the limited nature of many mineral resources have made mineral extraction sustainability a major issue. Deep learning and FinTech have provided creative answers to these problems in recent years. This presentation discusses the consequences of using deep learning-enabled FinTech solutions to identify minerals for sustainable natural resource extraction and use. Quantized deep learning for mineral identification during mining, using the widely used EfficientDet architecture, is proposed in this paper. We present a post-training quantization approach for the EfficientDet model that minimizes its size and complexity without compromising performance. We tested model efficiency with 8-bit and symmetric quantization. We also compare the quantized EfficientDet model to the floating-point model using a custom mineral dataset. The prescribed dataset gave the pre-train EfficientDet model 0.78 precision and 0.65 recall. The suggested model had 0.97 accuracy and 0.89 recall on the provided datasets. The suggested model is 99.5% accurate. Our results show that integrated FinTech technologies have enabled innovative financial models for ecologically friendly mineral extraction. By assessing environmental and social risks, FinTech platforms have helped investors assess mining project profitability. Investors may use this data to make informed judgments about sustainable development and ethical mining. Deep learning and FinTech mineral identification affects responsible natural resource development. These advancements enable environmentally and socially friendly mining. Data-driven insights and financial transparency can help the global mining business become more sustainable and responsible.

Suggested Citation

  • Radulescu, Magdalena & Dalal, Surjeet & Lilhore, Umesh Kumar & Saimiya, Sarita, 2024. "Optimizing mineral identification for sustainable resource extraction through hybrid deep learning enabled FinTech model," Resources Policy, Elsevier, vol. 89(C).
    • Handle: RePEc:eee:jrpoli:v:89:y:2024:i:c:s030142072400059x
    DOI: 10.1016/j.resourpol.2024.104692
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    References listed on IDEAS

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    1. Mingqiu Hou & Yuxiang Xiao & Zhengdong Lei & Zhi Yang & Yihuai Lou & Yuming Liu, 2023. "Machine Learning Algorithms for Lithofacies Classification of the Gulong Shale from the Songliao Basin, China," Energies, MDPI, vol. 16(6), pages 1-19, March.
    2. Evan Ross DeLancey & Jahan Kariyeva & Jason T Bried & Jennifer N Hird, 2019. "Large-scale probabilistic identification of boreal peatlands using Google Earth Engine, open-access satellite data, and machine learning," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-23, June.
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    Citations

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    Cited by:

    1. Nilashi, Mehrbakhsh & Ali Abumalloh, Rabab & Keng-Boon, Ooi & Wei-Han Tan, Garry & Cham, Tat-Huei & Cheng-Xi Aw, Eugene, 2024. "Unlocking sustainable resource management: A comprehensive SWOT and thematic analysis of FinTech with a focus on mineral management," Resources Policy, Elsevier, vol. 92(C).
    2. Işık, Cem & Bulut, Umit & Ongan, Serdar & Islam, Hasibul & Irfan, Muhammad, 2024. "Exploring how economic growth, renewable energy, internet usage, and mineral rents influence CO2 emissions: A panel quantile regression analysis for 27 OECD countries," Resources Policy, Elsevier, vol. 92(C).

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