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

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.