CHEMICAL MINING USING BIG DATA IMAGE ANALYTICS WITH DEEP THREE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK

A Ramana Lakshmi; Mudassir Khan; Ranichandra C; Papiya Mukherjee; Veeramalai Sankaradass; Madiajagan M

doi:10.31838/ecb/2022.11.11.012

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ECB. 2022; 11(11): 105-112

doi: 10.31838/ecb/2022.11.11.012

CHEMICAL MINING USING BIG DATA IMAGE ANALYTICS WITH DEEP THREE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK

A Ramana Lakshmi, Mudassir Khan, Ranichandra C, Papiya Mukherjee, Veeramalai Sankaradass, Madiajagan M.

Abstract
With intense competition that exists in the mining sector around the world, improvements in both product performance and energy economy are absolutely important. This presents a one-of-a-kind obstacle due to the fact that the demand for raw resources is constantly growing, despite the fact that vast supply of high-quality commodities are rapidly running out. The ability to correctly identify chemicals is a skill that is crucial to the investigation of a wide range of subjects. Conventional methods for determining the identity of chemicals take a significant amount of time and demand a substantial number of resources due to the fact that they impose a significant amount of reliance on the knowledge of the identifier as well as on external equipment. Technology based on deep learning has made it possible for people to identify chemicals in a way that requires significantly less
time and effort, as well as a significant reduction in the number of errors produced.

Key words: Chemical, Mining, Big Data, Three-Dimensional Convolutional Neural Network

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How to Cite this Article

Pubmed Style

A Ramana Lakshmi, Mudassir Khan, Ranichandra C, Papiya Mukherjee, Veeramalai Sankaradass, Madiajagan M. CHEMICAL MINING USING BIG DATA IMAGE ANALYTICS WITH DEEP THREE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK. ECB. 2022; 11(11): 105-112. doi:10.31838/ecb/2022.11.11.012

Web Style

A Ramana Lakshmi, Mudassir Khan, Ranichandra C, Papiya Mukherjee, Veeramalai Sankaradass, Madiajagan M. CHEMICAL MINING USING BIG DATA IMAGE ANALYTICS WITH DEEP THREE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK. https://www.eurchembull.com/?mno=131996 [Access: January 21, 2023]. doi:10.31838/ecb/2022.11.11.012

AMA (American Medical Association) Style

A Ramana Lakshmi, Mudassir Khan, Ranichandra C, Papiya Mukherjee, Veeramalai Sankaradass, Madiajagan M. CHEMICAL MINING USING BIG DATA IMAGE ANALYTICS WITH DEEP THREE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK. ECB. 2022; 11(11): 105-112. doi:10.31838/ecb/2022.11.11.012

Vancouver/ICMJE Style

A Ramana Lakshmi, Mudassir Khan, Ranichandra C, Papiya Mukherjee, Veeramalai Sankaradass, Madiajagan M. CHEMICAL MINING USING BIG DATA IMAGE ANALYTICS WITH DEEP THREE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK. ECB. (2022), [cited January 21, 2023]; 11(11): 105-112. doi:10.31838/ecb/2022.11.11.012

Harvard Style

A Ramana Lakshmi, Mudassir Khan, Ranichandra C, Papiya Mukherjee, Veeramalai Sankaradass, Madiajagan M (2022) CHEMICAL MINING USING BIG DATA IMAGE ANALYTICS WITH DEEP THREE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK. ECB, 11 (11), 105-112. doi:10.31838/ecb/2022.11.11.012

Turabian Style

A Ramana Lakshmi, Mudassir Khan, Ranichandra C, Papiya Mukherjee, Veeramalai Sankaradass, Madiajagan M. 2022. CHEMICAL MINING USING BIG DATA IMAGE ANALYTICS WITH DEEP THREE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK. European Chemical Bulletin, 11 (11), 105-112. doi:10.31838/ecb/2022.11.11.012

Chicago Style

A Ramana Lakshmi, Mudassir Khan, Ranichandra C, Papiya Mukherjee, Veeramalai Sankaradass, Madiajagan M. "CHEMICAL MINING USING BIG DATA IMAGE ANALYTICS WITH DEEP THREE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK." European Chemical Bulletin 11 (2022), 105-112. doi:10.31838/ecb/2022.11.11.012

MLA (The Modern Language Association) Style

APA (American Psychological Association) Style

A Ramana Lakshmi, Mudassir Khan, Ranichandra C, Papiya Mukherjee, Veeramalai Sankaradass, Madiajagan M (2022) CHEMICAL MINING USING BIG DATA IMAGE ANALYTICS WITH DEEP THREE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK. European Chemical Bulletin, 11 (11), 105-112. doi:10.31838/ecb/2022.11.11.012

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