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Artificial Neural Networks and Machine Learning,” Neurocomputing, vol. 121, pp. 1-4, Dec. 2013. https://doi.org/10.1016/j.neucom.2013.01.008 [5] M. Dalto, “Deep Neural Networks for Time Series Prediction with Application in Ultra-Short-Term Wind Forecasting,” IEEE, pp. 1657- 1663, 2015. [6] A. Ferreira and G. Giraldi, “Convolutional Neural Network Approaches to Granite Tiles Classification,” Expert Systems with Applications, vol. 84, pp. 1-11, Oct. 2017. https://doi.org/10.1016/j.eswa.2017.04.053 [7] Y. Bengio, “Learning Deep Architectures for AI,” Foundations and Trends

REFERENCES [1] H. H. Aghdam and E. J. Heravi, Guide to convolutional neural networks: a practical application to traffic-sign detection and classification . Cham, Switzerland: Springer, 2017. https://doi.org/10.1007/978-3-319-57550-6 [2] A. Gibson and J. Patterson, Deep Learning: A Practitioner’s Approach . O’Reilly Media, Inc., 2017. [3] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Computer Vision and Pattern Recognition , arXiv:1409.1556v6 [cs.CV], 2015. [4] A. Krizhevsky, I. Sutskever, and G. E. Hinton

] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., et al. (2019). A guide to deep learning in healthcare. Nature Medicine , 25 (1), 24–29. [7] Al-Ajlan, A., El Allali, A. (2018). CNN-MGP: Convolutional neural networks for metagenomics gene prediction. Interdisciplinary Sciences: Computational Life Sciences , doi.org/10.1007/s12539-018-0313-4. [8] Bursa, M., Lhotska, L. (2017). The use of convolutional neural networks in biomedical data processing. In Information Technology in Bio- and Medical Informatics . Springer, 100-119. [9

, Vol. 42, No. 4, pp. 722-737, 2015. [7] Schmidhuber, J., “Deep learning in neural networks: An overview,” Neural Networks , Vol. 61, pp. 85-117, 2015. [8] Shaheryar, A., Yin, X.-C., Yousuf, W., Robust Feature Extraction on Vibration Data under Deep-Learning Framework: An Application for Fault Identification in Rotary Machines, International Journal of Computer Applications, Vol. 167, No. 4, pp. 37-45, 2017. [9] Li, S., Liu, G., Tang, X., Lu, J., Hu, J., An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault

References [1] H. H. Aghdam and E. J. Heravi, Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and Classification. Cham, Switzerland: Springer, 2017. https://doi.org/10.1007/978-3-319-57550-6 [2] A. Gibson and J. Patterson, Deep Learning: A Practitioner’s Approach. O’Reilly Media, 2017. [3] S. Srinivas, R. K. Sarvadevabhatla, K. R. Mopuri, N. Prabhu, S. S. S. Kruthiventi, and R. V. Babu, “Chapter 2 - An Introduction to Deep Convolutional Neural Nets for Computer Vision,” in Deep Learning for Medical Image Analysis, S. K

with Deep Convolutional Neural Networks. – Advances in Neural Information Processing Systems, Vol. 25 , 2012, No 2. 12. Sun, Y., X. Wang, X. Tang. Deep Convolutional Network Cascade for Facial Point Detection. – In: Conference on Computer Vision and Pattern Recognition, Vol. 9 , 2013, No 3, pp. 3476-3483. 13. Eigen, D., C. Puhrsch, R. Fergus. Depth Map Prediction from a Single Image Using a Multi-Scale Deep Network. – Eprint Arxiv, 2014, pp. 2366-2374. 14. Kan, M., S. Shan, H. Chang, X. Chen. Stacked Progressive Auto-Encoder for Face Recognition. – In: IEEE

, “Convolutional feature learning and Hybrid CNN-HMM for scene number recognition,” Neurocomputing , vol. 184, 2016, pp. 78–90. https://doi.org/10.1016/j.neucom.2015.07.135 [4] M. Joo Er, Y. Zhang, N. Wang, and M. Pratama, “Attention pooling-based convolutional neural network for sentence modelling,” Information Sciences , vol. 373, 2016, pp. 388–403. https://doi.org/10.1016/j.ins.2016.08.084 [5] Z. Chen, F. Cao, and J. Hu, “Approximation by network operators with logistic activation functions,” Applied Mathematics and Computation , vol. 256, 2015, pp. 565–571. https

Classification with Deep Convolutional Neural Networks. – In: Advances in Neural Information Processing Systems, 2012, pp. 1097-1105. 14. Kumar, N., P. N. Belhumeur, A. Biswas, D. W. Jacobs, W. J. Kress, I. C. Lopez, J. V. Soares. Leafsnap: A Computer Vision System for Automatic Plant Species Identification. – In: Computer Vision ECCV’2012. Berlin, Heidelberg, Springer, 2012, pp. 502-516. 15. LeCun, B. B., J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel. Handwritten Digit Recognition with a Back-Propagation Network. – In: Advances in Neural Information

and Pattern Recognition (CVPR), Jun. 2016. https://doi.org/10.1109/cvpr.2016.90 [11] A. Krizhevsky, “One Weird Trick for Parallelizing Convolutional Neural Networks,” In CoRR, 2014. [12] K. Simonyan and A. Zisserman “Very Deep Convolutional Networks for Large-Scale Image Recognition,” In Proceedings of ICLR, 2015. [13] M. Li, T. Zhang, Y. Chen, and A. J. Smola, “Efficient Mini-Batch Training for Stochastic Optimization,” In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’14, 2014. http://dx.doi.org/10

Research, 10(Jul), pp.1755-1758. [8] Kingma, D. P. and Ba, J. 2014, ‘Adam: A method for stochastic optimization’, arXiv preprint arXiv:1412.6980. [9] Kowalski, M., Naruniec, J. and Trzcinski, T. 2017, ‘Deep alignment network: A convolutional neural network for robust face alignment’, CoRR abs/1706.01789. URL: http://arxiv.org/abs1706.01789 [10] Kowalski, M. and Naruniec, J. 2016, ‘Face alignment using k-cluster regression forests with weighted splitting’, IEEE Signal Processing Letters 23(11), 1567–1571. [11] Lee, D., Park, H. and Yoo, C. D. 2015, Face alignment using