Smart Mining System with Crystal Classification of Ores and Industrial Management

11 pages•Published: August 6, 2024

Mohamed Rizwan, Kalaiselvi K and Yashu Youwraj

Abstract

Mineral exploration is vital for ensuring a reliable source of raw materials that are necessary for contemporary living and the shift towards environmentally friendly technologies. The mining process entails costly procedures aimed at detecting regions with inherent mineral concentrations in the Earth's crust. Combining artificial intelligence and remote sensing techniques has the capacity to greatly decrease the expenses linked to these operations. Here, it presents a strong and intelligent model for mineral exploration that is specifically designed to identify possible areas to extract the desired composition of mineral. Our approach incorporates cutting-edge developments in artificial intelligence and remote sensing, and introduce a sophisticated deep-learning process that utilises a random forest algorithm to examine the dataset. The main goal is the find out the type of ores to be extracted from the given minerals. This technique has a wider scope than just identifying things. It can also be used to find the type of soil to extract the ores. This versatile method is not restricted to single ores and can be utilized for different ore deposit models and dataset types. The incorporation of deep learning into the analysis of ores data is a groundbreaking progress in the domain of mineral exploration. It can improve the efficiency, precision, and cost-effectiveness of identifying areas with abundant minerals, making a significant contribution to the sustainable acquisition of raw materials and the worldwide shift towards environmental.

Keyphrases: data privacy, data security, deep learning, machine learning, mining system, random forest

In: Rajakumar G (editor). Proceedings of 6th International Conference on Smart Systems and Inventive Technology, vol 19, pages 354-364.

Links:	https://easychair.org/publications/paper/qTx2
	https://doi.org/10.29007/fst4

BibTeX entry

@inproceedings{ICSSIT2024:Smart_Mining_System_with,
  author    = {Mohamed Rizwan and Kalaiselvi K and Yashu Youwraj},
  title     = {Smart Mining System with Crystal Classification of Ores and Industrial Management},
  booktitle = {Proceedings of 6th International Conference on Smart Systems and Inventive Technology},
  editor    = {Rajakumar G},
  series    = {Kalpa Publications in Computing},
  volume    = {19},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/qTx2},
  doi       = {10.29007/fst4},
  pages     = {354-364},
  year      = {2024}}

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