1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, GS., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., & Zheng, X. (2015). TensorFlow: large-scale machine learning on heterogeneous systems. https://www.tensorflow.org/, software available from tensorflow.org
2. Al-Shamari, A., Al-Sulaiman, S., Al-Mithin, A., Jarragh, A., Prakash, S.S., & Islam, M. (2012). Internal corrosion monitoring and management strategy for Kuwait’s pipeline network system. In: NACE corrosion conference, NACE, Salt Lake City
3. Amaral, J., Dos Santos Pinto, G. L., Vieira Pinheiro, G. R., Gomes Silva, V., & Ponciano Gomes, J. A. (2020). A non-intrusive system to classify the severity of damages caused by internal corrosion using the potential drop technique and electrical image mapping. Journal of Integrated Circuits and Systems, 15(3), 1–7. https://doi.org/10.29292/jics.v15i3.181.
4. Angayarkanni, N., & Durairaj, K. (2015). Euclidean distance transform (edt) algorithm applied to binary image for finding breast cancer. Biomedical and Pharmacology Journal, 8, 407–411.
5. ASTM (2018). Standard guide for examination and evaluation of pitting corrosion. ASTM, G46-94 (Reapproved 2018)