DEEP LEARNING APPROACHES FOR BIG DATA ANALYTICS: OPPORTUNITIES, ISSUES AND RESEARCH DIRECTIONS (26-33)

Список литературы: 1. Aliguliyev R.M., Hajirahimova M.Sh. Big Data phenomenon: Challenges and Opportunities// Problems of Information Technology, 2014, vol. 10, no. 2, pp. 316. 2. Aliguliyev R.M., Hajirahimova M.Sh, Aliyeva A.S. Current scientific and theoretical problems of Big Data// Problems of information society. 2016, no. 2, pp. 34–45. 3. Chen Xue-W. Big Data Deep Learning: Challenges and Perspectives// IEEE Access journal. 2014, vol. 2, pp. 514-525. 4. Najafabadi M., Villanustre F., Khoshgoftaar T. et al. Deep Learning applications and challenges in Big Data analytics// Journal of Big Data. 2015, vol.2, no.1, pp.2-21. 5. Elaraby N. M., Elmogy M., Barakat Sh. Deep Learning: Effective Tool for Big Data Analytics// International Journal of Computer Science Engineering. 2016, vol.5, no.5, pp. 254-262. 6. Jan B. Deep learning in Big Data Analytics: A comparative study// Computers and Electrical Engineering. 2017, vol.7, no. 24, pp. 1-13. 7. Zhang Q., Yang L. T., Chen Z. et al. A survey on deep learning for Big Data// Information Fusion, 2018, vol. 42, pp. 146–157. 8. Wang L., Alexander Ch.A. Machine Learning in Big Data// International Journal of Mathematical, Engineering and Management Sciences. 2016, vol. 1, no. 2, pp. 52–61. 9. Sivarajah U., Kamal M.M., Irani Z. et al. Critical analysis of Big Data challenges and analytical methods// Journal of Business Research. 2017, vol. 70, pp. 263–286. 10. Oussous A., Benjelloun F.-Z., Lahcen A. A. et al. Big Data technologies: A survey// Journal of King Saud University: Computer and Information Sciences. 2018, vol. 30, pp. 431–448. 11. Philip Chen C. L., Zhang C.-Y. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data// Information Sciences. 2014, vol. 275, no. 10, pp.314-347. 12. Tarwani K.M., Saudagar S.S., Misalkar H.D. Machine learning in Big Data analytics: an overview// International Journal of Advanced Research in Computer Science and Software Engineering. 2015, vol. 5, no. 4, pp. 270-274. 13. Suthaharan S. Big Data classification: problems and challenges in network intrusion prediction with machine learning// Performance Evaluation Review. 2014, vol. 41, no. 4, pp. 70-73. 14. Memudu M. T., Obidallah W., Raahemi B. Applying Deep Learning Techniques for Big Data Analytics: A Systematic Literature Review// Archives of Information Science and Technology. 2018, vol.1, no. 1, pp. 20-41. 15. Wu F., Wang Z., Zhang Z. et al. Weakly semisupervised deep learning for multi-label image annotation// IEEE Trans Big Data. 2015, vol.2, pp.109122. 16. Pouyanfar S., Chen S.C. T-LRA: Trend-based learning rate annealing for deep neural networks// Proceeding of the IEEE 3rd International Conference on Multimed Big Data (BigMM). 2017, pp. 50-57. 17. Graves A., Mohamed A., Hinton G. Speech recognition with Deep Recurrent Neural Networks// Proceeding of the IEEE International Conference on Acoustics, Speech and Signal Processing. 26-31 May 2013, pp. 6645 – 6649. DOI: 10.1109/ICASSP.2013.6638947 18. Cho K., Merrienboer B., Gulcehre C. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation// Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). 2014, pp. 1724–1734. 19. Chung J., Gülçehre C., Cho K. et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. 2014, https://arxiv.org/abs/1412.3555 20. Liu G., Bao H., Han B. A Stacked Autoencoder-Based Deep Neural Network for Achieving Gearbox Fault Diagnosis// Mathematical Problems in Engineering. 2018, vol. 2018, no. 5, pp. 110. 21. Hinton G.E., Osindero S., Teh Y.-W. A fast learning algorithm for deep belief nets// Neural computation, 2006, vol. 18, no. 7, pp. 1527–1554. 22. Zhang J., Han Y., Jiang J. Semi-supervised tensor learning for image classification// Multimedia Systems. 2017, vol. 23, no. 1, pp. 63-73. 23. Novikov A., Podoprikhin D., Osokin A. et al. Tensorizing neural netwroks// presented at the Advances in Neural Information Processing Systems, MIT, pp. 442–450, 2015. 24. Zhang Q., Yang L.T., Chen Z. Deep computation model for unsupervised feature learning on Big Data// IEEE Trans Services Comput. 2016, vol. 9, pp. 61–71. 25. Wang X., He Y. Learning from Uncertainty for Big Data: Future Analytical Challenges and Strategies// IEEE Systems, Man, & Cybernetics Magazine. April 2016, pp. 26-32. 26. Zheng Y. Urban Computing, Cambridge, The MİT Press, 2018, 609 p. 27. Ngiam J., Khosla A., Kim M. et al. Multimodal deep learning// Proceedings of the International Conference on Machine Learning, ACM, 2011, pp. 689–696. 28. Srivastava N., Salakhutdinov R. Multimodal learning with deep boltzmann machines// Proceedings of Advances in Neural Information Processing Systems, MIT. 2012, vol.25, pp. 2231–2239. 29. Ouyang W., Chu X., Wang X. Multi-source deep learning for human pose estimation// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE. 2014, pp. 2337–2344. 30. Zhang Q., Yang L. T., Chen Z. Deep computation model for unsupervised feature learning on Big Data// IEEE Transactions on Services Computing. 2016, vol.9, no.1, pp. 161–171. 31. Chen M., Xu Z.E., Weinberger K.Q. et al. Marginalized denoising autoencoders for domain adaptation// Proceeding of the 29th International Conference in Machine Learning, Edingburgh, Scotland, 2012. 32. Krizhevsky A., Sutskever I., Hinton G. Imagenet classification with deep convolutional neural networks// Advances in Neural Information Processing Systems. Curran Associates, Inc. 2012, vol. 25. pp 1106–1114. 33. Haupt J., Kahl S., Kowerko D. et al. LargeScale Plant Classification using Deep Convolutional Neural Networks, 2019, http://ceur-ws.org/Vol- 2125/paper_92.pdf) 34. Maggiori Y., Tarabalka G., Charpiat P.A. Convolutional neural networks for large-scale remotesensing image classification// IEEE Transactions on Geoscience and Remote Sensing. 2017, vol.55, no. 2, pp. 645–657. 35. Jia Z., Zaharia M., Aiken A. Beyond data and model parallelism for deep neural networks. arXiv:1807.05358v1 [cs.DC] 14 Jul 2018, pp.1-15. https://arxiv.org/pdf/1807.05358.pdf 36. Dean J., Corrado G. S., Chen K. et al. Large scale distributed deep networks// Proceedings of NIPS. 2012, pp. 1232–1240. 37. Sun P., Feng W., Han R. et al. Optimizing Network Performance for Distributed DNN Training on GPU Clusters: ImageNet/AlexNet Training in 1.5 Minutes/ arXiv preprint arXiv:1902.06855, 2019. 38. Coats A., Huval B., Wng T. et al. Deep learning with COTS HPC systems// J. Mach. Learn. Res. 2013, vol.28, pp. 1337–1345. 39. Zhou G., Sohn K., Lee H. Online incremental feature learning with denoising autoencoders/ Proceedings of the International Conference on Artificial Intelligence and Statistics. JMLR.org. 2012, pp 1453–1461. 40. Calandra R., Raiko T., Deisenroth M.P. et al. Learning deep belief networks from non-stationary streams/ Proceedings of the International Conference on Artificial Neural Networks and Machine Learning, Berlin Heidelberg, 2012, pp 379–386. 41. Li Y., Zhang M., Wang W. Online Real-Time Analysis of Data Streams Based on an Incremental High-Order Deep Learning Model// IEEE Access. 2018, vol. 6, pp. 77615 – 77623. 42. Wang R., Tao D. Non-local auto-encoder with collaborative stabilization for image restoration// IEEE Transactions on Image Processing. 2016, vol. 25, no. 5, pp. 2117–2129. 43. Mao X., Shen Ch., Yang Y.-B. Image restoration using very deep convolutional encoderdecoder networks with symmetric skip connections// Advances in Neural Information Processing Systems. 2016, vol. 29, pp. 2802–2810. 44. Zhang K., Zuo W., Chen Y. et al. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising// IEEE Transactions on Image Processing. 2017, vol. 26, no. 7, pp. 3142 – 31555. 45. Bu F., Chen Z., Zhang Q. Incomplete Big Data mpputation algorithm based on deep learning// Microelectronics & Computer. 2014, vol. 31, no. 12, pp. 173–176. 46. Valsesia D., Fracastoro G., Magli E. Image denoising with graph-Convolutional Neural Networks/ Proceeding of the 2019 IEEE International Conference on Image Processing (ICIP), 22-25 Sept. 2019, pp. 2399 - 2403.