(2020) ; “Advantages, disadvantages and application of SVM”. See also URL https://statinfer.com /204-6-7-soft-margin-classification-noisy-data/.## Aggarwal, Namita; and Agrawal, R. K.; (2012); “First and second order statistics features for classification of magnetic resonance brain images”. Journal of Signal and Information Processing, 3: 146-153. ## Alzubaidi, Laith; Zhang, Jinglan; Humaidi, Amjad J.; Al‑Dujaili, Ayad; Duan, Ye; Al‑Shamma, Omran; Santamaria, J. Fadhel; Mohammed A. ; Al‑Amidie, Muthana; and Farhan, Laith; (2021); “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions”. Journal of Big Data. See also URL https://doi.org/10.1186/s40537-021-00444-8. ## Bezdan, Timea; and Bačanin Džakula, Nebojsa; (2019) ; “Convolutional neural network layers and architectures”. International Scientific Conference On Information Technology And Data Related Research Data Science & Digital Broadcasting Systems. See also URL https://doi.org/10.15308/Sinteza-2019-445-451. ## Boas, Franz; (1922); “The measurement of differences between variable quantities”. Journal Of the American Statistical Association, XVIII(140): 425-445. ## Boato, Giulia; Dang-Nguyen; Duc-Tien; and De Natale, Francesco G.B; (2019); “Morphological Filter Detector for Image Forensics Applications”. IEEEXplore, 8:13549-13560. ## Chen, Jiayao; Yang, Tongjun; Zhang, Dongming; Huang, Hongwei; and Tian, Yu; (2021) ; “Deep learning based classification of rock structure of tunnel face”. Geoscience Frontiers,12( 1): 395-404. ## Chen, Lirong; Wang, Liang; Miao, Jinli; Gao, Huan; Zhang, Yue; Yao, Yao; Bai, Ming; Mei, Lisi; and He, Jing; (2020); “Review of the Application of Big Data and Artificial Intelligence in Geology”. Journal of Physics: Conference Series. See also URL https://doi.org/ 10.1088/1742-6596/1684/1 /012007. ## Chen,Yushi; Jiang, Hanlu; Li, Chunyang; Jia, Xiuping; and Ghamisi, Pedram; (2016); “Deep feature extraction and classification of hyperspectral images based on convolutional neural networks”. IEEE Transactions on Geoscience and Remote Sensing, 54(10): 6232–6251. See also URL https://doi.org/10. 1109/ TGRS.2016.2584107. ## Di Tommaso, Inés; and Rubinstein, Nora; (2007); “Hydrothermal alteration mapping using ASTER data in the Infiernillo porphyry deposit, Argentina”. Ore Geology Reviews, 32: 275–290. See also URL https://doi.org/ 10.1016/j.oregeorev.2006.05.004. ## Dumakor-Dupey, Nelson K. ; and Arya, Sampurna. (2021). “Machine Learning—A Review of Applications in Mineral Resource Estimation”. Energies, 14:1-29. See also URL https://doi.org/10.3390/en14144079. ## Elnemr, Heba Ahmed; Zayed, Nourhan Mohamed; and Fakhreldein, Mahmoud Abdelmoneim; (2015); “Handbook of research on emerging perspectives in intelligent pattern recognition, analysis, and image processing, feature extraction techniques: Fundamental concepts and survey”. See also URL https://doi.org/10.4018/978-1-4666-8654-0. ## Elsaid, Mahmoud; Aboelkhair, Hatem; Dardier, Ahmed; Hermas, Elsayed ;and Minoru, Urai; (2014); “Processing of multispectral ASTER data for mapping alteration minerals zones: As an aid for Uranium exploration in Elmissikat-Eleridiya Granites, Central Eastern Desert, Egypt”. The Open Geology Journal, 8:69-83. ## Evgeniou, Theodoros; and Pontil, Massimiliano; (2001) ; “Workshop on support vector machines: Theory and applications. Center for Biological and Computational Learning, and Artificial Intelligence Laboratory”. See also URL https://doi.org/ 10.1007/3-540-44673-7_12. ## https://blog.faradars.org/confusion-matrix-from-zer o-to-hero/ ## https://dastmardi.ir/1399/01/13/receiver_operating _characteristic/ ## https://www.mathworks.com/help/stats/support-vector-machines-for-binary-classification.html## Jogin, Manjunath; Mohana, M S; Madhulika, G Dv Divya, R K, Meghana; and S, Apoorvav (2018) ; “Feature extraction using convolution neural networks (CNN) and deep learning”. 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT-2018), MAY 18th & 19th. 1: 2319-2323. See also URL https://doi.org/ 10.1109/RTEICT42901.2018.9012507. ## K. Anuradhav Sankaranarayanan. Kv (2013); “Statistical feature exploration to classify oral cancers”. Journal of Global Research in Computer Science, 4(2): 8-12. ## Kamilaris, A.; and Prenafeta-Boldú, F. X. ; (2018); “A review of the use of convolutional neural networks in agriculture”. The Journal of Agricultural Science, See also URL https://doi.org/10.1017/S0021859618000436 1–11. ## Kang, Byeongcheol; and Lee, Kyungbook; (2020); “Managing uncertainty in geological scenarios using machine learning-based classification model on production data". Hindawi, 8892556: 1-16. ## Krizhevsky, Alex; Ilya, Sutskever; and Hinton, Geoffrey E. ; (2012); “Magenet classification with deep convolutional neural networks”. See also URL https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf. ## Kurama, Vihar; (2022); “A Review of Popular Deep Learning Architectures: AlexNet, VGG16, and GoogleNet”. https://blog.paperspace.com/popular-deep-learning-architectures-alexnet-vgg-googlenet/.## Ladwani, Vandana M.; (2017); “Support vector machines and applications”. See also URL https://doi.org/ 10.4018/978-1-5225-2498-4.ch012. ## Mahboob, M.A. ; Genc, B. ; Celik, T. v Ali, S. ; and Atif, I.; (2019); “Mapping hydrothermal minerals using remotely sensed reflectance spectroscopy data from Landsat”. The Journal of the Southern African Institute of Mining and Metallurgy. 119:279-289. ## Mohammadi, N. Mahvash; and Hezarkhani, A.; (2020); “Comparative study of SVM and RF methods for classification of alteration zones using remotely sensed data”. Journal of Mining and Environment (JME), 11(1): 49-61. See also URL https:// 10.22044/jme.2019.7956. 1664. ## Mutlag, Wamidh K. ; Ali, Shaker K., Aydam; Zahoor M. ; and Taher, Bahaa H.; (2020); “Feature extraction methods: A review”. Journal of Physics: Conference Series, .11-1: 1591 See also URL https://doi.org/10. 1088/1742-6596/1591/1/012028. ## Nathwani, Chetan L. ; Wilkinson, Jamie J. Fry; George, Armstrong; Robin N., Smith, Daniel J. ; and Ihlenfeld, Christian.; (2022); “Machine learning for geochemical exploration: classifying metallogenic fertility in arc magmas and insights into porphyry copper deposit formation”. Mineralium Deposita, See also URL https://doi.org/10.1007/s00126-021-01086-9. ## Nayak, Sunita; (2018); “Understanding AlexNet-LearnOpenCV”. See also URL https://learnopencv .com › understanding-alexnet. ## Nwaila, Glen; (2019); “Convolutional Neural Networks in Exploration of Mineral Deposits”. See also URL https://www.linkedin.com/pulse/convolutional-neural -networks-exploration-mineral-deposits-nwaila. ## Osuna, Edgar; Freund, Robert; Girosi, Federico; (1997); “Training support vector machines: an application to face detection”. Computer society conference on computer vision and pattern recognition, See also URL http://dx.doi.org/10.1109/ CVPR.1997. 609310. ## Rahimi, Hossain; Abedi, Maysam; Yousefi, Mahyar; Bahroudi, Abbas; and Elyas, Gholam-Reza; (2021); “Supervised mineral exploration targeting and the challenges withthe selection of deposit and non-deposit sites thereof”. Applied Geochemistry, See also URL https://doi.org/10.1016/j.apgeochem.2021.104940. ## “RBF SVM parameters”. See also URL https://scikit-learn.org/stable/_downloads/ea8b449d 469 9d078ef9cc5cded54cc67/plot_rbf_parameters.py. ## Sahbi, Hichem; and Geman, Donald; (2006); “A Hierarchy of Support Vector Machines for Pattern Detection”. Journal of Machine Learning Research, 7 (2006): 2087-2123. ## Shirmard, Hojat; Farahbakhsh, Ehsan; Dietmar Muller, R. ; and Chandra, Rohitash; (2021); “A review of machine learning in processing remote sensing data for mineral exploration”. See also URL arXiv:2103.07678 v2 [cs.LG] 4 Dec 2021. ## Tahmooresi, Mandana; (2021); “Data mining and intelligent optimization of support vector machine and convolutional neural network using genetic algorithm in order to modeling for mineral potential exploration (Case study: Gonabad arena) ”, Ph.D. Dissertation, Mahallat Branch, Islamic Azad University, Mahallat, IRAN. [In Persian]. ## Tahmooresi, Mandana; Babaei, Behnam; and Dehghan, Saeed; (2021); “Intelligent geochemical exploration modeling using multiclass support vector machine and integration it with continuous genetic algorithm in Gonabad region, Khorasan Razavi, Iran”.Arabian Journal of Geosciences See also URL https://doi.org/10.1007/s12517-021-07306-w. ## Tahmooresi, Mandana; Babaei, Behnam; and Dehghan, Saeed; (2022); “Geochemical exploration numerical modeling using convolutional neural network (Case study: Gonabad region)”. Journal of Aalytical and Numerical Methods in Mining Engineering (Yazd University) See also URL doi 10.29252/ANM.2022. 17958.1534. ## Tahmooresi, Mandana; Babaei, Behnam; and Dehghan, Saeed; (2022); “Mineral exploration modeling by convolutional neural network and continuous genetic algorithm: a case study in Khorasan Razavi, Iran”. Arabian Journal of Geosciences See also URL https:// doi .org /10.1007/s12517-022-10889-7## Tao, Jin; (2020); “Statistical object features”. See also URL https://slidetodoc.com/statistical-object-features-jin-tao-introduction. ## Vakili, Meysam; Ghamsari, Mohammad ;and Rezaei, Masoumeh; (2020); “Performance analysis and comparison of machine and deep learning algorithms for IoT data classification”. See also URL https://arxiv.org /abs/2001. 09636. ## Williams, Kylie; (2021); “What the –ic? An Introduction to Alteration”. https://www.geologyfor investors.com/ic-introduction-alteration/.## Wirth, Michael A. ; (2004); “Texture Analysis”. See also URL http://www.cyto.purdue.edu›education› wirth 06. ## You, Changhui; Zheng, Hong; Guo, Zhongyuanv Wang,Tianyu; and Wu, Xiongbin; (2021); “Multiscale content-independent feature fusion network for source camera identification”. Appl. Sci. 11(6752): 1-13. ## Zhang, Chun-Xia; Wei, Xiao-Li ;and Kim, Sang-Woon; (2021); “Empirical evaluation on utilizing CNN-features for seismic patch classification”. In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2021), 1: 166-173. ##