[1] Pi, Y., Nath, N.D. and Behzadan, A.H., 2020. Convolutional neural networks for object detection in aerial imagery for disaster response and recovery. Advanced Engineering Informatics, 43, pp. 101009.
[2] Dawood, M. and Asif, A., 2019. Deep-PHURIE: deep learning based hurricane intensity estimation from infrared satellite imagery. Neural Computing and Applications, pp. 1-9.
[3] Dotel, S., Shrestha, A., Bhusal, A., Pathak, R., Shakya, A. and Panday, S.P., 2020, March. Disaster Assessment from Satellite Imagery by Analysing Topographical Features Using Deep Learning. In Proceedings of the 2020 2nd International Conference on Image, Video and Signal Processing (pp. 86-92).
[4] Kaur, S., Gupta, S., Singh, S. and Gupta, I., 2021. Detection of Alzheimer’s Disease Using Deep Convolutional Neural Network. International Journal of Image and Graphics, pp. 2140012.
[5] Pritt, M. and Chern, G., 2017, October. Satellite image classification with deep learning. In 2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) pp. 1-7. IEEE.
[6] Cao, Q.D. and Choe, Y., 2020. Building damage annotation on post-hurricane satellite imagery based on convolutional neural networks. Natural Hazards, 103(3), pp. 3357-3376.
[7] Phung, V.H. and Rhee, E.J., 2019. A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets. Applied Sciences, 9(21), p.4500
[8] Chen, S.A., Escay, A., Haberland, C., Schneider, T., Staneva, V. and Choe, Y., 2018. Benchmark dataset for automatic damaged building detection from post-hurricane remotely sensed imagery. arXiv preprint arXiv:1812.05581.
[9] Doshi, J., Basu, S. and Pang, G., 2018. From satellite imagery to disaster insights. arXiv preprint, arXiv:1812.07033.
[10] Dotel, S., Shrestha, A., Bhusal, A., Pathak, R., Shakya, A. and Panday, S.P., 2020, March. Disaster Assessment from Satellite Imagery by Analysing Topographical Features Using Deep Learning. In Proceedings of the 2020 2nd International Conference on Image, Video and Signal Processing, pp. 86-92.
[11] Robertson, B.W., Johnson, M., Murthy, D., Smith, W.R. and Stephens, K.K., 2019. Using a combination of human insights and ‘deep learning’for real-time disaster communication. Progress in Disaster Science, 2, p.100030.
[12] Duarte, D., Nex, F., Kerle, N. and Vosselman, G., 2018. Satellite Image Classification Of Building Damages Using Airborne And Satellite Image Samples In A Deep Learning Approach. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 4(2).
[13] Pradhan, R., Aygun, R.S., Maskey, M., Ramachandran, R. and Cecil, D.J., 2017. Tropical cyclone intensity estimation using a deep convolutional neural network. IEEE Transactions on Image Processing, 27(2), pp.692-702.
[14] Li, Y., Hu, W., Dong, H. and Zhang, X., 2019. Building damage detection from post-event aerial imagery using single shot multibox detector. Applied Sciences, 9(6), p.1128.
[15] Li, Y., Ye, S. and Bartoli, I., 2018. Semisupervised classification of hurricane damage from postevent aerial imagery using deep learning. Journal of Applied Remote Sensing, 12(4), pp.045008.
[16] Nia, K.R. and Mori, G., 2017, May. Building damage assessment using deep learning and ground-level image data. In 2017 14th conference on computer and robot vision (CRV) (pp. 95-102). IEEE.
[17] Scannell, C.M., Veta, M., Villa, A.D., Sammut, E.C., Lee, J., Breeuwer, M. and Chiribiri, A., 2020. Deep‐learning‐based preprocessing for quantitative myocardial perfusion MRI. Journal of Magnetic Resonance Imaging, 51(6), pp.1689-1696.
[18] Zheng, X., Wang, M. and Ordieres-Meré, J., 2018. Comparison of data preprocessing approaches for applying deep learning to human activity recognition in the context of industry 4.0. Sensors, 18(7), p.2146.
[19] Ba, J.L., Kiros, J.R. and Hinton, G.E., 2016. Layer normalization. arXiv preprint arXiv:1607.06450.
[20] Perez, L. and Wang, J., 2017. The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621.
[21] El Naqa, I., Li, R. and Murphy, M.J. eds., 2015. Machine learning in radiation oncology: theory and applications. Springer.
[22] Shin, H.C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D. and Summers, R.M., 2016. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging, 35(5), pp.1285-1298.
[23] Li, Z., Gong, B. and Yang, T., 2016. Improved dropout for shallow and deep learning. Advances in neural information processing systems, 29, pp.2523-2531.
[24] Denil, M., Shakibi, B., Dinh, L., Ranzato, M.A. and De Freitas, N., 2013. Predicting parameters in deep learning. arXiv preprint arXiv:1306.0543.
[25] Betz, J.M., Brown, P.N. and Roman, M.C., 2011. Accuracy, precision, and reliability of chemical measurements in natural products research. Fitoterapia, 82(1), pp.44-52.
[26] Ng, B., Quinete, N. and Gardinali, P.R., 2020. Assessing accuracy, precision and selectivity using quality controls for non-targeted analysis. Science of The Total Environment, 713, p.136568.
[27] Handelman, G.S., Kok, H.K., Chandra, R.V., Razavi, A.H., Huang, S., Brooks, M., Lee,M.J. and Asadi, H., 2019. Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods. American Journal of Roentgenology, 212(1), pp.38-43.
[28] Hossin, M. and Sulaiman, M.N., 2015. A review on evaluation metrics for data classification evaluations. International Journal of Data Mining & Knowledge Management Process, 5(2), p.1.
[29] Choi, D., Shallue, C.J., Nado, Z., Lee, J., Maddison, C.J. and Dahl, G.E., 2019. On empirical comparisons of optimizers for deep learning. arXiv preprint arXiv:1910.05446.
[30] Lydia, A. and Francis, S., 2019. Adagrad—an optimizer for stochastic gradient descent. Int. J. Inf. Comput. Sci, 6(5).
[31] Duda, J., 2019. SGD momentum optimizer with step estimation by online parabola model. arXiv preprint arXiv:1907.07063.
[32] Wichrowska, O., Maheswaranathan, N., Hoffman, M.W., Colmenarejo, S.G., Denil, M., Freitas, N. and Sohl-Dickstein, J., 2017, July. Learned optimizers that scale and generalize. In International Conference on Machine Learning (pp. 3751-3760). PMLR.
[33] Kumar, A., Sarkar, S. and Pradhan, C., 2020. Malaria disease detection using cnn technique with sgd, rmsprop and adam optimizers. In Deep Learning Techniques for Biomedical and Health Informatics (pp. 211-230). Springer, Cham.
[34] Fürnkranz, J. and Flach, P.A., 2003. An analysis of rule evaluation metrics. In Proceedings of the 20th international conference on machine learning (ICML-03) (pp. 202-209).
[35] Zhou, J., Gandomi, A.H., Chen, F. and Holzinger, A., 2021. Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics, 10(5), p.593.