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Neural Computing and Applications

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09-11-2023 | Original Article

Prediction and classification of minerals using deep residual neural network

Authors: Prasannavenkatesan Theerthagiri, A. Usha Ruby, J. George Chellin Chandran

Published in: Neural Computing and Applications | Issue 4/2024

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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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Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73(16):5261–5267. https://doi.org/10.1128/AEM.00062-07CrossRef

Breiman L (1996) Bagging predictors. Mach Learn 24:123–140CrossRef

Gordon AD, Breiman L, Friedman JH, Olshen RA, Stone CJ (1984). Classification and Regression Trees. Wadsworth International Group, vol 40(3)

Patel AK, Chatterjee S, Gorai AK (2017) Development of machine vision-based ore classification model using support vector machine (SVM) algorithm. Arab J Geosci 10:1–16. https://doi.org/10.1007/s12517-017-2909-0CrossRef

Liu Y, Zhang Z, Liu X, Wang L, Xia X (2021) Ore image classification based on small deep learning model: Evaluation and optimization of model depth, model structure and data size. Miner Eng 172:107020. https://doi.org/10.1016/j.mineng.2021.107020CrossRef

Liu C, Li M, Zhang Y, Han S, Zhu Y (2019) An enhanced rock mineral recognition method integrating a deep learning model and clustering algorithm. Minerals 9(9):516. https://doi.org/10.3390/min9090516CrossRef

Zhang Y, Li M, Han S, Ren Q, Shi J (2019) Intelligent identification for rock-mineral microscopic images using ensemble machine learning algorithms. Sensors 19(18):3914. https://doi.org/10.3390/s19183914CrossRef

de Lima RP, Bonar A, Coronado DD, Marfurt K, Nicholson C (2019) Deep convolutional neural networks as a geological image classification tool. The Sedimentary Record 17(2):4–9. https://doi.org/10.2110/sedred.2019.2.4CrossRef

Fu Y, Aldrich C (2019) Quantitative ore texture analysis with convolutional neural networks. IFAC-PapersOnLine 52(14):99–104. https://doi.org/10.1016/j.ifacol.2019.09.171CrossRef

10.

Zhu S, Yang W, Hou G, Lu B, Wei S (2020) An intelligent classification and recognition method of rock thin section. Acta Petrol Siniva 40(1):106

11.

Iyas MR, Setiawan NI, Warmada IW (2020) Mask R-CNN for rock-forming minerals identification on petrography, case study at Monterado, west Kalimantan. In: E3S web of conferences. EDP Sciences. vol 200, p 06007. https://doi.org/10.1051/e3sconf/202020006007

12.

Si L, Xiong X, Wang Z, Tan C (2020) A deep convolutional neural network model for intelligent discrimination between coal and rocks in coal mining face. Math Probl Eng. https://doi.org/10.1155/2020/2616510CrossRef

13.

Bukharev A, Budennyy S, Lokhanova O, Belozerov B, Zhukovskaya E (2018) The task of instance segmentation of mineral grains in digital images of rock samples (thin sections). In: 2018 international conference on artificial intelligence applications and innovations (IC-AIAI). p 18–23. IEEE. https://doi.org/10.1109/IC-AIAI.2018.8674449.

14.

Zhang Z, Liu Y, Hu Q, Zhang Z, Liu Y (2020). Competitive voting-based multi-class prediction for ore selection. In: 2020 IEEE 16th international conference on automation science and engineering (CASE), p 514–519. IEEE. https://doi.org/10.1109/CASE48305.2020.9217017.

15.

Zhang Z, Liu Y, Hu Q, Zhang Z, Wang L, Liu X, Xia X (2020) Multi-information online detection of coal quality based on machine vision. Powder Technol 374:250–262. https://doi.org/10.1016/j.powtec.2020.07.040CrossRef

16.

Patel AK, Chatterjee S, Gorai AK (2019) Development of a machine vision system using the support vector machine regression (SVR) algorithm for the online prediction of iron ore grades. Earth Sci Inf 12:197–210. https://doi.org/10.1007/s12145-018-0370-6CrossRef

17.

Massinaei M, Jahedsaravani A, Taheri E, Khalilpour J (2019) Machine vision based monitoring and analysis of a coal column flotation circuit. Powder Technol 343:330–341. https://doi.org/10.1016/j.powtec.2018.11.056CrossRef

18.

Khorram F, Morshedy AH, Memarian H, Tokhmechi B, Zadeh HS (2017) Lithological classification and chemical component estimation based on the visual features of crushed rock samples. Arab J Geosci 10:1–9. https://doi.org/10.1007/s12517-017-3116-8CrossRef

19.

Galdames FJ, Perez CA, Estévez PA, Adams M (2019) Rock lithological classification by hyperspectral, range 3D and color images. Chemom Intell Lab Syst 189:138–148. https://doi.org/10.1016/j.chemolab.2019.04.006CrossRef

20.

Itano K, Ueki K, Iizuka T, Kuwatani T (2020) Geochemical discrimination of monazite source rock based on machine learning techniques and multinomial logistic regression analysis. Geosciences 10(2):63. https://doi.org/10.3390/geosciences10020063CrossRef

21.

Hasterok D, Gard M, Bishop CMB, Kelsey D (2019) Chemical identification of metamorphic protoliths using machine learning methods. Comput Geosci 132:56–68. https://doi.org/10.1016/j.cageo.2019.07.004CrossRef

22.

Lv Y, Le QT, Bui HB, Bui XN, Nguyen H, Nguyen-Thoi T, Song X (2020) A comparative study of different machine learning algorithms in predicting the content of ilmenite in titanium placer. Appl Sci 10(2):635. https://doi.org/10.3390/app10020635CrossRef

23.

Chen W, Su L, Chen X, Huang Z (2023) Rock image classification using deep residual neural network with transfer learning. Front Earth Sci 10:1079447CrossRef

24.

Agrawal N, Govil H (2023) A deep residual convolutional neural network for mineral classification. Adv Space Res 71(8):3186–3202CrossRef

25.

Wang J, Lin Z, Feng Z, Lu Y, Shi S (2021). Recognition of rock images and quantification of oil content using deep residual neural networks. In: 2021 4th international conference on signal processing and machine learning. p 60–65

26.

Theerthagiri P, Vidya J (2022) Cardiovascular disease prediction using recursive feature elimination and gradient boosting classification techniques. Expert Syst 39(9):e13064CrossRef

27.

Misra K (2012) Understanding mineral deposits. Springer Science & Business Media, Newyork

28.

El Haddad J, de Lima Filho ES, Vanier F, Harhira A, Padioleau C, Sabsabi M, Blouin A (2019) Multiphase mineral identification and quantification by laser-induced breakdown spectroscopy. Miner Eng 134:281–290. https://doi.org/10.1016/j.mineng.2019.02.025CrossRef

29.

Izadi H, Sadri J, Bayati M (2017) An intelligent system for mineral identification in thin sections based on a cascade approach. Comput Geosci 99:37–49. https://doi.org/10.1016/j.cageo.2016.10.010CrossRef

30.

Aligholi S, Lashkaripour GR, Khajavi R, Razmara M (2017) Automatic mineral identification using color tracking. Pattern Recogn 65:164–174. https://doi.org/10.1016/j.patcog.2016.12.012CrossRef

31.

Młynarczuk M, Górszczyk A, Ślipek B (2013) The application of pattern recognition in the automatic classification of microscopic rock images. Comput Geosci 60:126–133. https://doi.org/10.1016/j.cageo.2013.07.015CrossRef

32.

Aligholi S, Khajavi R, Razmara M (2015) Automated mineral identification algorithm using optical properties of crystals. Comput Geosci 85:175–183. https://doi.org/10.1016/j.cageo.2015.09.014CrossRef

33.

Ishikawa ST, Gulick VC (2013) An automated mineral classifier using Raman spectra. Comput Geosci 54:259–268. https://doi.org/10.1016/j.cageo.2013.01.011CrossRef

34.

Ślipek B, Młynarczuk M (2013) Application of pattern recognition methods to automatic identification of microscopic images of rocks registered under different polarization and lighting conditions. Geol, Geophys Environ 39(4):373–373. https://doi.org/10.7494/geol.2013.39.4.373CrossRef

35.

Harris D, Pan G (1999) Mineral favorability mapping: a comparison of artificial neural networks, logistic regression, and discriminant analysis. Nat Resour Res 8:93–109CrossRef

36.

Thompson S, Fueten F, Bockus D (2001) Mineral identification using artificial neural networks and the rotating polarizer stage. Comput Geosci 27(9):1081–1089. https://doi.org/10.1016/S0098-3004(00)00153-9CrossRef

37.

Solar M, Perez P, Watkins F (2008) Neural Recognition of Minerals. In: Artificial intelligence in theory and practice II: IFIP 20th world computer congress, TC 12: IFIP AI 2008 Stream, September 7-10, 2008, Milano, Italy 2, p 433–437. Springer US

38.

Zhang X, Zhou W, Karimi HR, Sun Y (2020) Finite-and fixed-time cluster synchronization of nonlinearly coupled delayed neural networks via pinning control. IEEE Trans Neural Netw Learn Syst 32(11):5222–5231. https://doi.org/10.1109/TNNLS.2020.3027312MathSciNetCrossRef

39.

Gao Y, Guo X, Yao R, Zhou W, Cattani C (2020) Stability analysis of neural network controller based on event triggering. J Franklin Inst 357(14):9960–9975. https://doi.org/10.1016/j.jfranklin.2020.07.040MathSciNetCrossRef

40.

Theerthagiri P (2022) Predictive analysis of cardiovascular disease using gradient boosting based learning and recursive feature elimination technique. Intel Syst Appl 16:200121

41.

Prasannavenkatesan T, Usha Ruby A (2022) RFFS: recursive random forest feature selection based ensemble algorithm for chronic kidney disease prediction. Expert Syst. https://doi.org/10.1111/exsy.13048CrossRef

Title: Prediction and classification of minerals using deep residual neural network
Authors: Prasannavenkatesan Theerthagiri
A. Usha Ruby
J. George Chellin Chandran
Publication date: 09-11-2023
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 4/2024
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-023-09141-4

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