[1] Marvinney, R. G. (2015). Overview of Maine metallic mineral deposits and mining. Maine Geological Survey Circular, 15-9.
[2] Drzymala, J., Kowalczuk, P. B., Oteng-Peprah, M., Foszcz, D., Muszer, A., Henc, T., & Luszczkiewicz, A. (2013). Application of the grade-recovery curve in the batch flotation of Polish copper ore. Minerals Engineering, 49, 17-23.
[3] Barbian, N., Cilliers, J. J., Morar, S. H., & Bradshaw, D. J. (2007). Froth imaging, air recovery and bubble loading to describe flotation bank performance. International Journal of Mineral Processing, 84(1-4), 81-88.
[4] Aldrich, C., Marais, C., Shean, B. J., & Cilliers, J. J. (2010). Online monitoring and control of froth flotation systems with machine vision: A review. International Journal of Mineral Processing, 96(1-4), 1-13.
[5] Nakhaei, F., Irannajad, M., & Mohammadnejad, S. (2023). A comprehensive review of froth surface monitoring as an aid for grade and recovery prediction of flotation process. Part A: Structural features. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 45(1), 2587-2605.
[6] Jahedsaravani,A., Marhaban, M., & Massinaei, M. (2014). Prediction of the metallurgical performances of a batch flotation system by image analysis and neural networks. Minerals Engineering, 69, 137-145.
[7] Massinaei, M. (2015). Estimation of metallurgical parameters of flotation process from froth visual features. International Journal of Mining & Geo-Engineering, 49, 75-81.
[8] Jahedsaravani,A., Marhaban, M., & Massinaei, M. (2016). Application of statistical and intelligent techniques for modeling of metallurgical performance of a batch flotation process. Chemical Engineering Communications, 203, 151-160.
[9] Chang, Y. T., Lin, J., Shieh, J. S., & Abbod, M. F. (2012). Optimization the initial weights of artificial neural networks via genetic algorithm applied to hip bone fracture prediction. Advances in Fuzzy Systems, 2012(6), 1-9.
[10] Zarie, M., Jahedsaravani, A., & Massinaei, M. (2020). Flotation froth image classification using convolutional neural networks. Minerals Engineering, 155, 106443.
[11] Allahkarami, E., Salmani Nuri, O., Abdollahzadeh, A., Rezai, B., Maghsoudi, B., (2017). “Improving estimation accuracy of metallurgical performance of industrial flotation process by using hybrid genetic algorithm - artificial neural network (GA-ANN)”. Physicochemical problems of mineral processing, 53(1), 366–378.
[12] Hung, C. C., Song, E., Lan, Y., Hung, C. C., Song, E., & Lan, Y. (2019). Image texture, texture features, and image texture classification and segmentation. Image Texture Analysis: Foundations, Models and Algorithms, 3-14.
[13] Wen, Z., Zhou, C., Pan, J., Nie, T., Jia, R., & Yang, F. (2021). Froth image feature engineering-based prediction method for concentrate ash content of coal flotation. Minerals Engineering, 170, 107023.
[14] García-Lamont, F., Cervantes, J., López-Chau, A., & Ruiz-Castilla, S. (2020). Color image segmentation using saturated RGB colors and decoupling the intensity from the hue. Multimedia Tools and Applications, 79, 1555-1584.
[15] Rismayana, A. H., Alfianti, H., & Ramdan, D. S. (2022). Facial Skin Color Segmentation Using Otsu Thresholding Algorithm. Journal of Applied Intelligent System, 7(1), 26-35.
[16] Shiping, M. (2018). A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model, 2018. Sensors, 18, 3583.
[17] Mendenhall W., Sincich T., (2019). A second course in statistics: regression analysis. Pearson, 8th edition.