Literature Review of Deep Learning Research Areas

Year 2019, , 188 - 215, 30.12.2019

M. Mutlu Yapıcı , Adem Tekerek , Nurettin Topaloğlu

https://doi.org/10.30855/gmbd.2019.03.01

Abstract

Deep learning
(DL) is a powerful machine learning field that has achieved considerable
success in many research areas. Especially in the last decade,
the-state-of-the-art studies on many research areas such as computer vision,
object recognition, speech recognition, natural language processing were led to
the awakening of the artificial intelligence from deep sleep. Nowadays, many
researchers are trying to find solutions to many problems in various fields
under the light of DL methods. In this study, we present important knowledge to
guide about DL models and challenging topics which can be used in DL for
researchers. We investigated DL studies which are made in the most popular and
challenging fields such as Autonomous Vehicles, Natural Language Processing, Handwritten
Character Recognition, Signature Verification, Voice and Video Recognition,
Medical İmage Processing, Big Data. Furthermore, we point out the remaining
challenges of these research areas these can be solved by DL and discuss the
future topics in order to help the researchers. The contribution of this study
is that to list the most challenging subjects that can be studied with DL. We
believe that researchers will contribute to these issues by achieving
successful results through DL algorithms. The goal of this work is to help them
make informed decisions about the best DL model that fits the needs and
resources of researchers seeking to work with DL.

Keywords

Deep learning, Convolutional Neural networks, Recurrent Neural Networks

Derin Öğrenme Araştırma Alanlarının Literatür Taraması

Abstract

Derin öğrenme
(Deep Learning-DL), birçok alanda önemli başarılar elde etmiş güçlü bir makine
öğrenmesi yöntemidir. Özellikle son on yılda, bilgisayarlı görü, nesne tanıma,
konuşma tanıma, doğal dil işleme gibi birçok araştırma alanında başarılı
sonuçlar elde ederek, yapay zekanın derin uykudan uyanmasına yol açmıştır.
Günümüzde, çeşitli alanlardaki birçok araştırmacı, DL yöntemlerini kullanarak
alanlarında en iyi sonucu almaya çalışmaktadır. Bu tarama çalışmasında, DL
modelleri ve DL ile çalışılabilecek önemli araştırma konuları hakkında  bilgiler vererek araştırmacılara rehberlik
etmeyi hedefliyoruz. Çalışmada Özerk Araçlar (Autonomous Vehicles), Doğal Dil
İşleme (Natural Language Processing), El Yazısı Karakter Tanıma (Handwritten
Character Recognition), İmza Doğrulama (Signature Verification), Ses ve Video
Tanıma (Voice and Video Recognition), Tıbbi Görüntü İşleme (Medical İmage
Processing), Büyük Veri (Big Data) gibi dünyanın en popüler ve en zorlu
alanlarında yapılan DL çalışmalarını inceliyoruz. Ayrıca, araştırmacılara
yardımcı olmak için, incelediğimiz bu alanlardaki DL ile çalışılabilecek, henüz
çalışılmamış veya yeterince iyi sonuçlar elde edilememiş problemlere dikkat
çekerek olası araştırma konularını listeliyoruz. Bu çalışmanın nihai amacı, DL
ile çalışmak isteyen araştırmacılara umut vadeden yeni konuları gösterebilmek
ve, araştırmacıların ihtiyaçlarına uyan en iyi DL modelini seçebilmeleri için
modeller hakkında bilinçli kararlar vermelerine yardımcı olmaktır.

Keywords

Derin Öğrenme, Evrişimsel Sinir Ağları, Tekrarlayan Sinir Ağları

References

• [1] P. P. de San Roman, J. Benois-Pineau, J.-P. Domenger, F. Paclet, D. Cataert, and A. de Rugy, “Saliency Driven Object recognition in egocentric videos with deep CNN: toward application in assistance to Neuroprostheses,” Comput. Vis. Image Underst., vol. 164, pp. 82–91, 2017.[2] Z. Zhang, X. Liu, and Y. Cui, “Multi-phase Offline Signature Verification System Using Deep Convolutional Generative Adversarial Networks,” in 2016 9th International Symposium on Computational Intelligence and Design (ISCID), 2016, vol. 2, pp. 103–107.[3] J. Maria, J. Amaro, G. Falcao, and L. A. Alexandre, “Stacked Autoencoders Using Low-Power Accelerated Architectures for Object Recognition in Autonomous Systems,” Neural Process. Lett., vol. 43, no. 2, pp. 445–458, 2016.[4] H. Kaya, F. Gürpınar, and A. A. Salah, “Video-based emotion recognition in the wild using deep transfer learning and score fusion,” Image Vis. Comput., vol. 65, pp. 66–75, 2017.[5] R. Al-Jawfi, “Handwriting Arabic character recognition LeNet using neural network.,” Int. Arab J. Inf. Technol., vol. 6, no. 3, pp. 304–309, 2009.[6] B. Ribeiro, I. Gonçalves, S. Santos, and A. Kovacec, “Deep learning networks for off-line handwritten signature recognition,” in Iberoamerican Congress on Pattern Recognition, 2011, pp. 523–532.[7] X. Sun et al., “Transferring deep knowledge for object recognition in Low-quality underwater videos,” Neurocomputing, vol. 275, pp. 897–908, 2018.[8] S. Pereira, A. Pinto, V. Alves, and C. A. Silva, “Brain tumor segmentation using convolutional neural networks in MRI images,” IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1240–1251, 2016.[9] M. Ribeiro, A. E. Lazzaretti, and H. S. Lopes, “A study of deep convolutional auto-encoders for anomaly detection in videos,” Pattern Recognit. Lett., vol. 105, pp. 13–22, 2018.[10] M. Al-Ayyoub, A. Nuseir, K. Alsmearat, Y. Jararweh, and B. Gupta, “Deep learning for Arabic NLP: A survey,” J. Comput. Sci., 2017.[11] A. Mayr, G. Klambauer, T. Unterthiner, and S. Hochreiter, “DeepTox: Toxicity Prediction using Deep Learning,” Front. Environ. Sci., vol. 3, p. 80, 2016.[12] D. Li and Z. Wang, “Video Superresolution via Motion Compensation and Deep Residual Learning,” IEEE Trans. Comput. Imaging, vol. 3, no. 4, pp. 749–762, 2017.[13] I. Kiral-Kornek et al., “Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System,” EBioMedicine, 2017.[14] A. A. A. Setio et al., “Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks,” IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1160–1169, May 2016.[15] C. Wu, W. Fan, Y. He, J. Sun, and S. Naoi, “Handwritten Character Recognition by Alternately Trained Relaxation Convolutional Neural Network,” in 2014 14th International Conference on Frontiers in Handwriting Recognition, 2014, pp. 291–296.[16] J. Kawahara and G. Hamarneh, “Multi-resolution-Tract CNN with Hybrid Pretrained and Skin-Lesion Trained Layers,” in Machine Learning in Medical Imaging, 2016, pp. 164–171.[17] X. Song, T. Rui, S. Zhang, J. Fei, and X. Wang, “A road segmentation method based on the deep auto-encoder with supervised learning,” Comput. Electr. Eng., vol. 68, pp. 381–388, 2018.[18] S. Alghyaline, J. W. Hsieh, and C. H. Chuang, “Video action classification using symmelets and deep learning,” in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017, pp. 414–419.[19] E. Cambria and B. White, “Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article],” IEEE Comput. Intell. Mag., vol. 9, no. 2, pp. 48–57, May 2014.[20] A. Khatami, M. Babaie, A. Khosravi, H. R. Tizhoosh, and S. Nahavandi, “Parallel deep solutions for image retrieval from imbalanced medical imaging archives,” Appl. Soft Comput., vol. 63, pp. 197–205, 2018.[21] R. Socher, “Recursive deep learning for natural language processing and computer vision,” Citeseer, 2014.[22] M. Al-Qizwini, I. Barjasteh, H. Al-Qassab, and H. Radha, “Deep learning algorithm for autonomous driving using GoogLeNet,” in 2017 IEEE Intelligent Vehicles Symposium (IV), 2017, pp. 89–96.[23] E. Nasr-Esfahani et al., “Segmentation of vessels in angiograms using convolutional neural networks,” Biomed. Signal Process. Control, vol. 40, pp. 240–251, 2018.[24] S. Ramos, S. Gehrig, P. Pinggera, U. Franke, and C. Rother, “Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling,” in 2017 IEEE Intelligent Vehicles Symposium (IV), 2017, pp. 1025–1032.[25] A. Wang, J. Lu, J. Cai, T. J. Cham, and G. Wang, “Large-Margin Multi-Modal Deep Learning for RGB-D Object Recognition,” IEEE Trans. Multimed., vol. 17, no. 11, pp. 1887–1898, 2015.[26] A. Uçar, Y. Demir, and C. Güzeliş, “Moving towards in object recognition with deep learning for autonomous driving applications,” in 2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA), 2016, pp. 1–5.[27] X. Zhang, X. Li, J. An, L. Gao, B. Hou, and C. Li, “Natural language description of remote sensing images based on deep learning,” in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017, pp. 4798–4801.[28] I. Abroug and N. E. Ben Amara, “Off-line signature verification systems: Recent advances,” in International Image Processing, Applications and Systems Conference, 2014, pp. 1–6.[29] A. Işın, C. Direkoğlu, and M. Şah, “Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods,” Procedia Comput. Sci., vol. 102, pp. 317–324, 2016.[30] D. Ciregan, U. Meier, and J. Schmidhuber, “Multi-column deep neural networks for image classification,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 3642–3649.[31] F. I. Vancea, A. D. Costea, and S. Nedevschi, “Vehicle taillight detection and tracking using deep learning and thresholding for candidate generation,” in 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), 2017, pp. 267–272.[32] L. G. Hafemann, R. Sabourin, and L. S. Oliveira, “Offline handwritten signature verification #x2014; Literature review,” in 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), 2017, pp. 1–8.[33] F. Milletari et al., “Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound,” Comput. Vis. Image Underst., vol. 164, pp. 92–102, 2017.[34] W. Ouyang, X. Chu, and X. Wang, “Multi-source Deep Learning for Human Pose Estimation,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 2337–2344.[35] L. G. Hafemann, R. Sabourin, and L. S. Oliveira, “Writer-independent feature learning for Offline Signature Verification using Deep Convolutional Neural Networks,” in 2016 International Joint Conference on Neural Networks (IJCNN), 2016, pp. 2576–2583.[36] G. Prabhakar, B. Kailath, S. Natarajan, and R. Kumar, “Obstacle detection and classification using deep learning for tracking in high-speed autonomous driving,” in 2017 IEEE Region 10 Symposium (TENSYMP), 2017, pp. 1–6.[37] A. Becerra, J. I. de la Rosa, E. González, A. D. Pedroza, J. M. Martínez, and N. I. Escalante, “Speech recognition using deep neural networks trained with non-uniform frame-level cost functions,” in 2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), 2017, pp. 1–6.[38] Đ. T. Grozdić, S. T. Jovičić, and M. Subotić, “Whispered speech recognition using deep denoising autoencoder,” Eng. Appl. Artif. Intell., vol. 59, pp. 15–22, 2017.[39] S. Tamura et al., “Audio-visual speech recognition using deep bottleneck features and high-performance lipreading,” in 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2015, pp. 575–582.[40] R. Sarikaya, G. E. Hinton, and A. Deoras, “Application of Deep Belief Networks for Natural Language Understanding,” IEEE/ACM Trans. Audio, Speech, Lang. Process., vol. 22, no. 4, pp. 778–784, 2014.[41] R. Galinsky, A. Alekseev, and S. I. Nikolenko, “Improving neural network models for natural language processing in russian with synonyms,” in 2016 IEEE Artificial Intelligence and Natural Language Conference (AINL), 2016, pp. 1–7.[42] H. Zhuang, C. Wang, C. Li, Q. Wang, and X. Zhou, “Natural Language Processing Service Based on Stroke-Level Convolutional Networks for Chinese Text Classification,” in 2017 IEEE International Conference on Web Services (ICWS), 2017, pp. 404–411.[43] S. Iamsa-at and P. Horata, “Handwritten Character Recognition Using Histograms of Oriented Gradient Features in Deep Learning of Artificial Neural Network,” in 2013 International Conference on IT Convergence and Security (ICITCS), 2013, pp. 1–5.[44] X. Xiao, L. Jin, Y. Yang, W. Yang, J. Sun, and T. Chang, “Building fast and compact convolutional neural networks for offline handwritten Chinese character recognition,” Pattern Recognit., vol. 72, pp. 72–81, 2017.[45] I.-J. Kim and X. Xie, “Handwritten Hangul recognition using deep convolutional neural networks,” Int. J. Doc. Anal. Recognit., vol. 18, no. 1, pp. 1–13, Mar. 2015.[46] S. Zheng et al., “Sunspot drawings handwritten character recognition method based on deep learning,” New Astron., vol. 45, pp. 54–59, 2016.[47] H. Feng and C. C. Wah, “Online signature verification using a new extreme points warping technique,” Pattern Recognit. Lett., vol. 24, no. 16, pp. 2943–2951, 2003.[48] D. Bertolini, L. S. Oliveira, E. Justino, and R. Sabourin, “Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers,” Pattern Recognit., vol. 43, no. 1, pp. 387–396, 2010.[49] G. Rigoll and A. Kosmala, “A systematic comparison between on-line and off-line methods for signature verification with hidden Markov models,” in Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170), 1998, vol. 2, pp. 1755–1757 vol.2.[50] P. Porwik, R. Doroz, and T. Orczyk, “Signatures verification based on PNN classifier optimised by PSO algorithm,” Pattern Recognit., vol. 60, pp. 998–1014, 2016.[51] R. Kumar, J. D. Sharma, and B. Chanda, “Writer-independent off-line signature verification using surroundedness feature,” Pattern Recognit. Lett., vol. 33, no. 3, pp. 301–308, 2012.[52] W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” Bull. Math. Biophys., vol. 5, no. 4, pp. 115–133, 1943.[53] G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Comput., vol. 18, no. 7, pp. 1527–1554, 2006.[54] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, 2012, pp. 1097–1105.[55] Google, “TensorFlow.” [Online]. Available: https://www.tensorflow.org/.[56] M. Abadi et al., “Tensorflow: Large-scale machine learning on heterogeneous distributed systems,” arXiv Prepr. arXiv1603.04467, 2016.[57] Facebook, “FAIR,” https://research.fb.com/fair-open-sources-deep-learning-modules-for-torch/. .[58] C. Microsoft, “Computational Network Toolkit (CNTK),” 2016. [Online]. Available: https://www.microsoft.com/en-us/cognitive-toolkit/.[59] D. S. Banerjee, K. Hamidouche, and D. K. Panda, “Re-Designing CNTK Deep Learning Framework on Modern GPU Enabled Clusters,” in 2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), 2016, pp. 144–151.[60] NVIDIA, “Caffe2 Deep Learning Framework,” https://developer.nvidia.com/caffe2, 2017. .[61] Y. Jia et al., “Caffe: Convolutional architecture for fast feature embedding,” in Proceedings of the 22nd ACM international conference on Multimedia, 2014, pp. 675–678.[62] S. Shi, Q. Wang, P. Xu, and X. Chu, “Benchmarking State-of-the-Art Deep Learning Software Tools,” in 2016 7th International Conference on Cloud Computing and Big Data (CCBD), 2016, pp. 99–104.[63] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, p. 436, 2015.[64] S. Min, B. Lee, and S. Yoon, “Deep learning in bioinformatics,” Brief. Bioinform., vol. 18, no. 5, pp. 851–869, 2017.[65] V. N. Nguyen, R. Jenssen, and D. Roverso, “Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning,” Int. J. Electr. Power Energy Syst., vol. 99, pp. 107–120, 2018.[66] G. Litjens et al., “A survey on deep learning in medical image analysis,” Med. Image Anal., vol. 42, pp. 60–88, 2017.[67] Z. Li, X. Zhang, H. Müller, and S. Zhang, “Large-scale retrieval for medical image analytics: A comprehensive review,” Med. Image Anal., vol. 43, pp. 66–84, 2018.[68] D. Shen, G. Wu, and H.-I. Suk, “Deep Learning in Medical Image Analysis,” Annu. Rev. Biomed. Eng., vol. 19, no. 1, pp. 221–248, 2017.[69] Z. Hu, J. Tang, Z. Wang, K. Zhang, L. Zhang, and Q. Sun, “Deep learning for image-based cancer detection and diagnosis − A survey,” Pattern Recognit., vol. 83, pp. 134–149, 2018.[70] H. Fang, Z. Zhang, C. J. Wang, M. Daneshmand, C. Wang, and H. Wang, “A survey of big data research,” IEEE Netw., vol. 29, no. 5, pp. 6–9, 2015.[71] Q. Zhang, L. T. Yang, Z. Chen, and P. Li, “A survey on deep learning for big data,” Inf. Fusion, vol. 42, pp. 146–157, 2018.[72] A. R. Sharma and P. Kaushik, “Literature survey of statistical, deep and reinforcement learning in natural language processing,” in 2017 International Conference on Computing, Communication and Automation (ICCCA), 2017, pp. 350–354.[73] A. Sanmorino and S. Yazid, “A survey for handwritten signature verification,” in 2012 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering, 2012, pp. 54–57.[74] B. Zitová and J. Flusser, “Image registration methods: A survey,” Image Vis. Comput., vol. 21, no. 11, pp. 977–1000, 2003.[75] P. Wang, W. Li, P. Ogunbona, J. Wan, and S. Escalera, “RGB-D-based human motion recognition with deep learning: A survey,” Comput. Vis. Image Underst., 2018.[76] J. Wang, Y. Chen, S. Hao, X. Peng, and L. Hu, “Deep learning for sensor-based activity recognition: A Survey,” Pattern Recognit. Lett., 2018.[77] S. Purushotham, C. Meng, Z. Che, and Y. Liu, “Benchmarking deep learning models on large healthcare datasets,” J. Biomed. Inform., vol. 83, pp. 112–134, 2018.[78] P. Meyer, V. Noblet, C. Mazzara, and A. Lallement, “Survey on deep learning for radiotherapy,” Comput. Biol. Med., vol. 98, pp. 126–146, 2018.[79] P. Li, D. Wang, L. Wang, and H. Lu, “Deep visual tracking: Review and experimental comparison,” Pattern Recognit., vol. 76, pp. 323–338, 2018.[80] S. Khan and T. Yairi, “A review on the application of deep learning in system health management,” Mech. Syst. Signal Process., vol. 107, pp. 241–265, 2018.[81] A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Comput. Electron. Agric., vol. 147, pp. 70–90, 2018.[82] S. Herath, M. Harandi, and F. Porikli, “Going deeper into action recognition: A survey,” Image Vis. Comput., vol. 60, pp. 4–21, 2017.[83] Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew, “Deep learning for visual understanding: A review,” Neurocomputing, vol. 187, pp. 27–48, 2016.[84] P. S. Grewal, F. Oloumi, U. Rubin, and M. T. S. Tennant, “Deep learning in ophthalmology: a review,” Can. J. Ophthalmol., 2018.[85] A. Garcia-Garcia, S. Orts-Escolano, S. Oprea, V. Villena-Martinez, P. Martinez-Gonzalez, and J. Garcia-Rodriguez, “A survey on deep learning techniques for image and video semantic segmentation,” Appl. Soft Comput., vol. 70, pp. 41–65, 2018.[86] O. Faust, Y. Hagiwara, T. J. Hong, O. S. Lih, and U. R. Acharya, “Deep learning for healthcare applications based on physiological signals: A review,” Comput. Methods Programs Biomed., vol. 161, pp. 1–13, 2018.[87] H. Chen, O. Engkvist, Y. Wang, M. Olivecrona, and T. Blaschke, “The rise of deep learning in drug discovery,” Drug Discov. Today, 2018.[88] K. Shi, H. Bao, and N. Ma, “Forward Vehicle Detection Based on Incremental Learning and Fast R-CNN,” in 2017 13th International Conference on Computational Intelligence and Security (CIS), 2017, pp. 73–76.[89] X. Du, M. H. Ang, and D. Rus, “Car detection for autonomous vehicle: LIDAR and vision fusion approach through deep learning framework,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017, pp. 749–754.[90] A. Soin and M. Chahande, “Moving vehicle detection using deep neural network,” in 2017 International Conference on Emerging Trends in Computing and Communication Technologies (ICETCCT), 2017, pp. 1–5.[91] V. D. Nguyen, H. Van Nguyen, D. T. Tran, S. J. Lee, and J. W. Jeon, “Learning Framework for Robust Obstacle Detection, Recognition, and Tracking,” IEEE Trans. Intell. Transp. Syst., vol. 18, no. 6, pp. 1633–1646, 2017.[92] N. Deepika and V. V. S. Variyar, “Obstacle classification and detection for vision based navigation for autonomous driving,” in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017, pp. 2092–2097.[93] A. Dairi, F. Harrou, M. Senouci, and Y. Sun, “Unsupervised obstacle detection in driving environments using deep-learning-based stereovision,” Rob. Auton. Syst., vol. 100, pp. 287–301, 2018.[94] C. Chen, H. Xiang, T. Qiu, C. Wang, Y. Zhou, and V. Chang, “A rear-end collision prediction scheme based on deep learning in the Internet of Vehicles,” J. Parallel Distrib. Comput., 2017.[95] Y. F. Chen, M. Liu, M. Everett, and J. P. How, “Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning,” in 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017, pp. 285–292.[96] W. Huang, G. Song, H. Hong, and K. Xie, “Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning,” IEEE Trans. Intell. Transp. Syst., vol. 15, no. 5, pp. 2191–2201, 2014.[97] Y. Jia, J. Wu, and Y. Du, “Traffic speed prediction using deep learning method,” in 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 2016, pp. 1217–1222.[98] Y. Jia, J. Wu, M. Ben-Akiva, R. Seshadri, and Y. Du, “Rainfall-integrated traffic speed prediction using deep learning method,” IET Intell. Transp. Syst., vol. 11, no. 9, pp. 531–536, 2017.[99] A. Koesdwiady, R. Soua, and F. Karray, “Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach,” IEEE Trans. Veh. Technol., vol. 65, no. 12, pp. 9508–9517, 2016.[100] J. Wang, Q. Gu, J. Wu, G. Liu, and Z. Xiong, “Traffic Speed Prediction and Congestion Source Exploration: A Deep Learning Method,” in 2016 IEEE 16th International Conference on Data Mining (ICDM), 2016, pp. 499–508.[101] X. Du, H. Zhang, H. V Nguyen, and Z. Han, “Stacked LSTM Deep Learning Model for Traffic Prediction in Vehicle-to-Vehicle Communication,” in 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), 2017, pp. 1–5.[102] Y. Liu, H. Zheng, X. Feng, and Z. Chen, “Short-term traffic flow prediction with Conv-LSTM,” in 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP), 2017, pp. 1–6.[103] N. G. Polson and V. O. Sokolov, “Deep learning for short-term traffic flow prediction,” Transp. Res. Part C Emerg. Technol., vol. 79, pp. 1–17, 2017.[104] Y. Lv, Y. Duan, W. Kang, Z. Li, and F. Y. Wang, “Traffic Flow Prediction With Big Data: A Deep Learning Approach,” IEEE Trans. Intell. Transp. Syst., vol. 16, no. 2, pp. 865–873, 2015.[105] Y. Duan, Y. Lv, and F. Y. Wang, “Performance evaluation of the deep learning approach for traffic flow prediction at different times,” in 2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), 2016, pp. 223–227.[106] J. n. Xin, X. Du, and J. Zhang, “Deep learning for robust outdoor vehicle visual tracking,” in 2017 IEEE International Conference on Multimedia and Expo (ICME), 2017, pp. 613–618.[107] R. Hadsell, A. Erkan, P. Sermanet, M. Scoffier, U. Muller, and Y. LeCun, “Deep belief net learning in a long-range vision system for autonomous off-road driving,” in 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008, pp. 628–633.[108] V. Rausch, A. Hansen, E. Solowjow, C. Liu, E. Kreuzer, and J. K. Hedrick, “Learning a deep neural net policy for end-to-end control of autonomous vehicles,” in 2017 American Control Conference (ACC), 2017, pp. 4914–4919.[109] W. Xia, H. Li, and B. Li, “A Control Strategy of Autonomous Vehicles Based on Deep Reinforcement Learning,” in 2016 9th International Symposium on Computational Intelligence and Design (ISCID), 2016, vol. 2, pp. 198–201.[110] T. Zhang, G. Kahn, S. Levine, and P. Abbeel, “Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search,” in 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016, pp. 528–535.[111] M. F. Yahya and M. R. Arshad, “Detection of markers using deep learning for docking of autonomous underwater vehicle,” in 2017 IEEE 2nd International Conference on Automatic Control and Intelligent Systems (I2CACIS), 2017, pp. 179–184.[112] R. Yu, Z. Shi, C. Huang, T. Li, and Q. Ma, “Deep reinforcement learning based optimal trajectory tracking control of autonomous underwater vehicle,” in 2017 36th Chinese Control Conference (CCC), 2017, pp. 4958–4965.[113] R. Sarikaya, G. E. Hinton, and B. Ramabhadran, “Deep belief nets for natural language call-routing,” in 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, pp. 5680–5683.[114] N. Dethlefs, “Domain Transfer for Deep Natural Language Generation from Abstract Meaning Representations,” IEEE Comput. Intell. Mag., vol. 12, no. 3, pp. 18–28, 2017.[115] Y. LeCun et al., “Handwritten digit recognition with a back-propagation network,” in Advances in neural information processing systems, 1990, pp. 396–404.[116] Y. Bengio, Y. LeCun, C. Nohl, and C. Burges, “LeRec: A NN/HMM Hybrid for On-Line Handwriting Recognition,” Neural Comput., vol. 7, no. 6, pp. 1289–1303, 1995.[117] S. Espana-Boquera, M. J. Castro-Bleda, J. Gorbe-Moya, and F. Zamora-Martinez, “Improving Offline Handwritten Text Recognition with Hybrid HMM/ANN Models,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 4, pp. 767–779, 2011.[118] F. Yin, Q. F. Wang, X. Y. Zhang, and C. L. Liu, “ICDAR 2013 Chinese Handwriting Recognition Competition,” in 2013 12th International Conference on Document Analysis and Recognition, 2013, pp. 1464–1470.[119] M. M. R. Sazal, S. K. Biswas, M. F. Amin, and K. Murase, “Bangla handwritten character recognition using deep belief network,” in 2013 International Conference on Electrical Information and Communication Technology (EICT), 2014, pp. 1–5.[120] V. Pham, T. Bluche, C. Kermorvant, and J. Louradour, “Dropout Improves Recurrent Neural Networks for Handwriting Recognition,” in 2014 14th International Conference on Frontiers in Handwriting Recognition, 2014, pp. 285–290.[121] P. Doetsch, M. Kozielski, and H. Ney, “Fast and Robust Training of Recurrent Neural Networks for Offline Handwriting Recognition,” in 2014 14th International Conference on Frontiers in Handwriting Recognition, 2014, pp. 279–284.[122] D. C. Ciresan, U. Meier, L. M. Gambardella, and J. Schmidhuber, “Convolutional Neural Network Committees for Handwritten Character Classification,” in 2011 International Conference on Document Analysis and Recognition, 2011, pp. 1135–1139.[123] D. Cireşan and U. Meier, “Multi-Column Deep Neural Networks for offline handwritten Chinese character classification,” in 2015 International Joint Conference on Neural Networks (IJCNN), 2015, pp. 1–6.[124] L. Chen, S. Wang, W. Fan, J. Sun, and S. Naoi, “Beyond human recognition: A CNN-based framework for handwritten character recognition,” in 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), 2015, pp. 695–699.[125] W. Yang, L. Jin, D. Tao, Z. Xie, and Z. Feng, “DropSample: A new training method to enhance deep convolutional neural networks for large-scale unconstrained handwritten Chinese character recognition,” Pattern Recognit., vol. 58, pp. 190–203, 2016.[126] S. Huang, Z. Zhong, L. Jin, S. Zhang, and H. Wang, “DropRegion training of inception font network for high-performance Chinese font recognition,” Pattern Recognit., vol. 77, pp. 395–411, 2018.[127] C. Boufenar, A. Kerboua, and M. Batouche, “Investigation on deep learning for off-line handwritten Arabic character recognition,” Cogn. Syst. Res., 2017.[128] A. Trivedi, S. Srivastava, A. Mishra, A. Shukla, and R. Tiwari, “Hybrid evolutionary approach for Devanagari handwritten numeral recognition using Convolutional Neural Network,” Procedia Comput. Sci., vol. 125, pp. 525–532, 2018.[129] M. Soomro, M. A. Farooq, and R. H. Raza, “Performance Evaluation of Advanced Deep Learning Architectures for Offline Handwritten Character Recognition,” in 2017 International Conference on Frontiers of Information Technology (FIT), 2017, pp. 362–367.[130] J. Sueiras, V. Ruiz, A. Sanchez, and J. F. Velez, “Offline continuous handwriting recognition using sequence to sequence neural networks,” Neurocomputing, vol. 289, pp. 119–128, 2018.[131] A. K. Jain, F. D. Griess, and S. D. Connell, “On-line signature verification,” Pattern Recognit., vol. 35, no. 12, pp. 2963–2972, 2002.[132] Y. Qi and B. R. Hunt, “Signature verification using global and grid features,” Pattern Recognit., vol. 27, no. 12, pp. 1621–1629, 1994.[133] M. A. Shouman, N. Lashin, and H. M. Hamza, “OFFLINE SIGNATURE VERIFICATION BASED ON DIFFERENT SETS OF FEATURES.”[134] R. Doroz, P. Porwik, and T. Orczyk, “Dynamic signature verification method based on association of features with similarity measures,” Neurocomputing, vol. 171, pp. 921–931, 2016.[135] M. Fayyaz, M. Hajizadeh_Saffar, M. Sabokrou, and M. Fathy, “Feature representation for online signature verification,” arXiv Prepr. arXiv1505.08153, 2015.[136] M. Liwicki et al., “Signature Verification Competition for Online and Offline Skilled Forgeries (SigComp2011),” in 2011 International Conference on Document Analysis and Recognition, 2011, pp. 1480–1484.[137] M. Liwicki, M. I. Malik, L. Alewijnse, E. v. d. Heuvel, and B. Found, “ICFHR 2012 Competition on Automatic Forensic Signature Verification (4NsigComp 2012),” in 2012 International Conference on Frontiers in Handwriting Recognition, 2012, pp. 823–828.[138] M. I. Malik, M. Liwicki, L. Alewijnse, W. Ohyama, M. Blumenstein, and B. Found, “ICDAR 2013 Competitions on Signature Verification and Writer Identification for On- and Offline Skilled Forgeries (SigWiComp 2013),” in 2013 12th International Conference on Document Analysis and Recognition, 2013, pp. 1477–1483.[139] Y. M. Al-Omari, S. N. H. S. Abdullah, and K. Omar, “State-of-the-art in offline signature verification system,” in 2011 International Conference on Pattern Analysis and Intelligence Robotics, 2011, vol. 1, pp. 59–64.[140] A. Soleimani, B. N. Araabi, and K. Fouladi, “Deep Multitask Metric Learning for Offline Signature Verification,” Pattern Recognit. Lett., vol. 80, pp. 84–90, 2016.[141] L. G. Hafemann, R. Sabourin, and L. S. Oliveira, “Analyzing features learned for Offline Signature Verification using Deep CNNs,” in 2016 23rd International Conference on Pattern Recognition (ICPR), 2016, pp. 2989–2994.[142] L. G. Hafemann, R. Sabourin, and L. S. Oliveira, “Learning features for offline handwritten signature verification using deep convolutional neural networks,” Pattern Recognit., vol. 70, pp. 163–176, 2017.[143] S. Tayeb et al., “Toward data quality analytics in signature verification using a convolutional neural network,” in 2017 IEEE International Conference on Big Data (Big Data), 2017, pp. 2644–2651.[144] R. Tolosana, R. Vera-Rodriguez, J. Fierrez, and J. Ortega-Garcia, “Exploring Recurrent Neural Networks for On-Line Handwritten Signature Biometrics,” IEEE Access, vol. 6, pp. 5128–5138, 2018.[145] Y.-W. Tan, W.-J. Liu, W. Jiang, and H. Zheng, “Integration of articulatory knowledge and voicing features based on DNN/HMM for Mandarin speech recognition,” in 2015 International Joint Conference on Neural Networks (IJCNN), 2015, pp. 1–8.[146] B. Wu et al., “An End-to-End Deep Learning Approach to Simultaneous Speech Dereverberation and Acoustic Modeling for Robust Speech Recognition,” IEEE J. Sel. Top. Signal Process., vol. 11, no. 8, pp. 1289–1300, 2017.[147] Y.-H. Tu, J. Du, Q. Wang, X. Bao, L.-R. Dai, and C.-H. Lee, “An information fusion framework with multi-channel feature concatenation and multi-perspective system combination for the deep-learning-based robust recognition of microphone array speech,” Comput. Speech Lang., vol. 46, pp. 517–534, 2017.[148] T. Gao, J. Du, L.-R. Dai, and C.-H. Lee, “A unified DNN approach to speaker-dependent simultaneous speech enhancement and speech separation in low SNR environments,” Speech Commun., vol. 95, pp. 28–39, 2017.[149] H. M. Fayek, M. Lech, and L. Cavedon, “Evaluating deep learning architectures for Speech Emotion Recognition,” Neural Networks, vol. 92, pp. 60–68, 2017.[150] K. Mannepalli, P. N. Sastry, and M. Suman, “A novel Adaptive Fractional Deep Belief Networks for speaker emotion recognition,” Alexandria Eng. J., vol. 56, no. 4, pp. 485–497, 2017.[151] Q. Zhang, X. Chen, Q. Zhan, T. Yang, and S. Xia, “Respiration-based emotion recognition with deep learning,” Comput. Ind., vol. 92–93, pp. 84–90, 2017.[152] T. Bhowmik, A. Chowdhury, and S. K. Das Mandal, “Deep Neural Network based Place and Manner of Articulation Detection and Classification for Bengali Continuous Speech,” Procedia Comput. Sci., vol. 125, pp. 895–901, 2018.[153] L. Deng, D. Yu, and J. Platt, “Scalable stacking and learning for building deep architectures,” in Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, 2012, pp. 2133–2136.[154] S. Achanta and S. V Gangashetty, “Deep Elman recurrent neural networks for statistical parametric speech synthesis,” Speech Commun., vol. 93, pp. 31–42, 2017.[155] I. Ariav, D. Dov, and I. Cohen, “A deep architecture for audio-visual voice activity detection in the presence of transients,” Signal Processing, vol. 142, pp. 69–74, 2018.[156] R. Olmos, S. Tabik, and F. Herrera, “Automatic handgun detection alarm in videos using deep learning,” Neurocomputing, vol. 275, pp. 66–72, 2018.[157] M. Parchami, S. Bashbaghi, E. Granger, and S. Sayed, “Using deep autoencoders to learn robust domain-invariant representations for still-to-video face recognition,” in 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2017, pp. 1–6.[158] Y. Rao, J. Lu, and J. Zhou, “Attention-Aware Deep Reinforcement Learning for Video Face Recognition,” in 2017 IEEE International Conference on Computer Vision (ICCV), 2017, pp. 3951–3960.[159] Y. Akbulut, A. Şengür, Ü. Budak, and S. Ekici, “Deep learning based face liveness detection in videos,” in 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), 2017, pp. 1–4.[160] B. Cheng et al., “Robust emotion recognition from low quality and low bit rate video: A deep learning approach,” in 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII), 2017, pp. 65–70.[161] O. Gupta, D. Raviv, and R. Raskar, “Illumination invariants in deep video expression recognition,” Pattern Recognit., vol. 76, pp. 25–35, 2018.[162] Y. Han, P. Zhang, T. Zhuo, W. Huang, and Y. Zhang, “Going deeper with two-stream ConvNets for action recognition in video surveillance,” Pattern Recognit. Lett., 2017.[163] E. P. Ijjina and K. M. Chalavadi, “Human action recognition in RGB-D videos using motion sequence information and deep learning,” Pattern Recognit., vol. 72, pp. 504–516, 2017.[164] G. I. Parisi, J. Tani, C. Weber, and S. Wermter, “Lifelong learning of human actions with deep neural network self-organization,” Neural Networks, vol. 96, pp. 137–149, 2017.[165] M. Ma, N. Marturi, Y. Li, A. Leonardis, and R. Stolkin, “Region-sequence based six-stream CNN features for general and fine-grained human action recognition in videos,” Pattern Recognit., vol. 76, pp. 506–521, 2018.[166] W. Huang, H. Ding, and G. Chen, “A novel deep multi-channel residual networks-based metric learning method for moving human localization in video surveillance,” Signal Processing, vol. 142, pp. 104–113, 2018.[167] M. Iliadis, L. Spinoulas, and A. K. Katsaggelos, “Deep fully-connected networks for video compressive sensing,” Digit. Signal Process., vol. 72, pp. 9–18, 2018.[168] H. Mohsen, E.-S. A. El-Dahshan, E.-S. M. El-Horbaty, and A.-B. M. Salem, “Classification using Deep Learning Neural Networks for Brain Tumors,” Futur. Comput. Informatics J., 2017.[169] X. Gao, W. Li, M. Loomes, and L. Wang, “A fused deep learning architecture for viewpoint classification of echocardiography,” Inf. Fusion, vol. 36, pp. 103–113, 2017.[170] T. Kooi et al., “Large scale deep learning for computer aided detection of mammographic lesions,” Med. Image Anal., vol. 35, pp. 303–312, 2017.[171] M. Saha, C. Chakraborty, and D. Racoceanu, “Efficient Deep Learning Model for Mitosis Detection using Breast Histopathology Images,” Comput. Med. Imaging Graph., 2017.[172] J. Wang and Y. Yang, “A context-sensitive deep learning approach for microcalcification detection in mammograms,” Pattern Recognit., 2018.[173] J. I. Orlando, E. Prokofyeva, M. del Fresno, and M. B. Blaschko, “An ensemble deep learning based approach for red lesion detection in fundus images,” Comput. Methods Programs Biomed., vol. 153, pp. 115–127, 2018.[174] C. Wang, L. Gong, Q. Yu, X. Li, Y. Xie, and X. Zhou, “DLAU: A scalable deep learning accelerator unit on FPGA,” IEEE Trans. Comput. Des. Integr. Circuits Syst., vol. 36, no. 3, pp. 513–517, 2017.[175] S. Hussain, S. M. Anwar, and M. Majid, “Segmentation of glioma tumors in brain using deep convolutional neural network,” Neurocomputing, 2017.[176] X. Zhao, Y. Wu, G. Song, Z. Li, Y. Zhang, and Y. Fan, “A deep learning model integrating FCNNs and CRFs for brain tumor segmentation,” Med. Image Anal., vol. 43, pp. 98–111, 2018.[177] S. Valverde et al., “Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach,” Neuroimage, vol. 155, pp. 159–168, 2017.[178] A. Birenbaum and H. Greenspan, “Multi-view longitudinal CNN for multiple sclerosis lesion segmentation,” Eng. Appl. Artif. Intell., vol. 65, pp. 111–118, 2017.[179] B. D. Barkana, I. Saricicek, and B. Yildirim, “Performance analysis of descriptive statistical features in retinal vessel segmentation via fuzzy logic, ANN, SVM, and classifier fusion,” Knowledge-Based Syst., vol. 118, pp. 165–176, 2017.[180] S. Miao, Z. J. Wang, and R. Liao, “A CNN Regression Approach for Real-Time 2D/3D Registration,” IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1352–1363, May 2016.[181] X. Yang, R. Kwitt, M. Styner, and M. Niethammer, “Quicksilver: Fast predictive image registration – A deep learning approach,” Neuroimage, vol. 158, pp. 378–396, 2017.[182] H. Jia, Y. Xia, Y. Song, W. Cai, M. Fulham, and D. D. Feng, “Atlas registration and ensemble deep convolutional neural network-based prostate segmentation using magnetic resonance imaging,” Neurocomputing, vol. 275, pp. 1358–1369, 2018.[183] A. Qayyum, S. M. Anwar, M. Awais, and M. Majid, “Medical image retrieval using deep convolutional neural network,” Neurocomputing, vol. 266, pp. 8–20, 2017.[184] X. Yuan, L. Xie, and M. Abouelenien, “A regularized ensemble framework of deep learning for cancer detection from multi-class, imbalanced training data,” Pattern Recognit., vol. 77, pp. 160–172, 2018.[185] Y. Xiao, J. Wu, Z. Lin, and X. Zhao, “A deep learning-based multi-model ensemble method for cancer prediction,” Comput. Methods Programs Biomed., vol. 153, pp. 1–9, 2018.[186] H. Chougrad, H. Zouaki, and O. Alheyane, “Deep Convolutional Neural Networks for Breast Cancer Screening,” Comput. Methods Programs Biomed., 2018.[187] Y. Wang, H. Mao, and Z. Yi, “Protein secondary structure prediction by using deep learning method,” Knowledge-Based Syst., vol. 118, pp. 115–123, 2017.[188] T. Du, L. Liao, C. H. Wu, and B. Sun, “Prediction of residue-residue contact matrix for protein-protein interaction with Fisher score features and deep learning,” Methods, vol. 110, pp. 97–105, 2016.[189] S. Wang, Z. Li, Y. Yu, and J. Xu, “Folding Membrane Proteins by Deep Transfer Learning,” Cell Syst., vol. 5, no. 3, p. 202–211.e3, 2017.[190] L. Wei, Y. Ding, R. Su, J. Tang, and Q. Zou, “Prediction of human protein subcellular localization using deep learning,” J. Parallel Distrib. Comput., 2017.[191] K. Tian, M. Shao, Y. Wang, J. Guan, and S. Zhou, “Boosting compound-protein interaction prediction by deep learning,” Methods, vol. 110, pp. 64–72, 2016.[192] T. Unterthiner, A. Mayr, G. Klambauer, and S. Hochreiter, “Toxicity prediction using deep learning,” arXiv Prepr. arXiv1503.01445, 2015.[193] R. Huang et al., “Tox21Challenge to Build Predictive Models of Nuclear Receptor and Stress Response Pathways as Mediated by Exposure to Environmental Chemicals and Drugs,” Front. Environ. Sci., vol. 3, p. 85, 2016.[194] A. Koutsoukas, J. St. Amand, M. Mishra, and J. Huan, “Predictive Toxicology: Modeling Chemical Induced Toxicological Response Combining Circular Fingerprints with Random Forest and Support Vector Machine,” Front. Environ. Sci., vol. 4, p. 11, 2016.[195] L. Kuang, L. Yang, and Y. Liao, “An Integration Framework on Cloud for Cyber Physical Social Systems Big Data,” IEEE Trans. Cloud Comput., 2015.[196] S. K. Bansal and S. Kagemann, “Integrating Big Data: A Semantic Extract-Transform-Load Framework,” Computer (Long. Beach. Calif)., vol. 48, no. 3, pp. 42–50, Mar. 2015.[197] G. Bello-Orgaz, J. J. Jung, and D. Camacho, “Social big data: Recent achievements and new challenges,” Inf. Fusion, vol. 28, pp. 45–59, 2016.[198] B. T. Hazen, C. A. Boone, J. D. Ezell, and L. A. Jones-Farmer, “Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications,” Int. J. Prod. Econ., vol. 154, pp. 72–80, 2014.[199] B. Hutchinson, L. Deng, and D. Yu, “Tensor Deep Stacking Networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1944–1957, 2013.[200] J. Dean et al., “Large scale distributed deep networks,” in Advances in neural information processing systems, 2012, pp. 1223–1231.[201] X.-W. Chen and X. Lin, “Big data deep learning: challenges and perspectives,” IEEE access, vol. 2, pp. 514–525, 2014.[202] A. Coates, B. Huval, T. Wang, D. Wu, B. Catanzaro, and N. Andrew, “Deep learning with COTS HPC systems,” in International Conference on Machine Learning, 2013, pp. 1337–1345.[203] C. Zhang, P. Li, G. Sun, Y. Guan, B. Xiao, and J. Cong, “Optimizing fpga-based accelerator design for deep convolutional neural networks,” in Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2015, pp. 161–170.[204] J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng, “Multimodal deep learning,” in Proceedings of the 28th international conference on machine learning (ICML-11), 2011, pp. 689–696.[205] N. Srivastava and R. R. Salakhutdinov, “Multimodal learning with deep boltzmann machines,” in Advances in neural information processing systems, 2012, pp. 2222–2230.[206] L. Zhao, Q. Hu, and W. Wang, “Heterogeneous Feature Selection With Multi-Modal Deep Neural Networks and Sparse Group LASSO,” IEEE Trans. Multimed., vol. 17, no. 11, pp. 1936–1948, 2015.[207] Q. Zhang, L. T. Yang, and Z. Chen, “Deep Computation Model for Unsupervised Feature Learning on Big Data,” IEEE Trans. Serv. Comput., vol. 9, no. 1, pp. 161–171, 2016.[208] S. Wan and L. E. Banta, “Parameter incremental learning algorithm for neural networks,” IEEE Trans. Neural Networks, vol. 17, no. 6, pp. 1424–1438, 2006.[209] L. Zhao, J. Chen, F. Chen, W. Wang, C. T. Lu, and N. Ramakrishnan, “SimNest: Social Media Nested Epidemic Simulation via Online Semi-Supervised Deep Learning,” in 2015 IEEE International Conference on Data Mining, 2015, pp. 639–648.[210] L. Yu, H. Chen, Q. Dou, J. Qin, and P. A. Heng, “Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos,” IEEE J. Biomed. Heal. Informatics, vol. 21, no. 1, pp. 65–75, 2017.[211] G. Zhou, K. Sohn, and H. Lee, “Online incremental feature learning with denoising autoencoders,” in Artificial Intelligence and Statistics, 2012, pp. 1453–1461.[212] P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and Composing Robust Features with Denoising Autoencoders,” in Proceedings of the 25th International Conference on Machine Learning, 2008, pp. 1096–1103.[213] P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol, “Stacked denoising autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion,” J. Mach. Learn. Res., vol. 11, pp. 3371–3408, 2010.[214] R. Wang and D. Tao, “Non-Local Auto-Encoder With Collaborative Stabilization for Image Restoration,” IEEE Trans. Image Process., vol. 25, no. 5, pp. 2117–2129, May 2016.[215] B. Jan et al., “Deep learning in big data Analytics: A comparative study,” Comput. Electr. Eng., 2017.[216] G. Qi, Z. Zhu, K. Erqinhu, Y. Chen, Y. Chai, and J. Sun, “Fault-diagnosis for reciprocating compressors using big data and machine learning,” Simul. Model. Pract. Theory, vol. 80, pp. 104–127, 2018.[217] H. Cui, L. Zhang, R. Kang, and X. Lan, “Research on fault diagnosis for reciprocating compressor valve using information entropy and SVM method,” J. Loss Prev. Process Ind., vol. 22, no. 6, pp. 864–867, 2009.[218] A. Widodo and B.-S. Yang, “Support vector machine in machine condition monitoring and fault diagnosis,” Mech. Syst. Signal Process., vol. 21, no. 6, pp. 2560–2574, 2007.[219] G.-M. Xian and B.-Q. Zeng, “An intelligent fault diagnosis method based on wavelet packer analysis and hybrid support vector machines,” Expert Syst. Appl., vol. 36, no. 10, pp. 12131–12136, 2009.[220] B.-S. Yang, W.-W. Hwang, D.-J. Kim, and A. C. Tan, “Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines,” Mech. Syst. Signal Process., vol. 19, no. 2, pp. 371–390, 2005.[221] N. K. Verma, A. Roy, and A. Salour, “An optimized fault diagnosis method for reciprocating air compressors based on SVM,” in 2011 IEEE International Conference on System Engineering and Technology, 2011, pp. 65–69.[222] V. T. Tran, F. AlThobiani, and A. Ball, “An approach to fault diagnosis of reciprocating compressor valves using Teager–Kaiser energy operator and deep belief networks,” Expert Syst. Appl., vol. 41, no. 9, pp. 4113–4122, 2014.[223] M. Havaei et al., “Brain tumor segmentation with deep neural networks,” Med. Image Anal., vol. 35, pp. 18–31, 2017.[224] Z. Jiang, H. Zhang, Y. Wang, and S.-B. Ko, “Retinal blood vessel segmentation using fully convolutional network with transfer learning,” Comput. Med. Imaging Graph., vol. 68, pp. 1–15, 2018.[225] A. F. M. Oliveira, S. R. M. Pereira, and C. A. B. Silva, “Retinal Vessel Segmentation based on Fully Convolutional Neural Networks,” Expert Syst. Appl., p. , 2018.[226] S. Moccia, E. De Momi, S. El Hadji, and L. S. Mattos, “Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics,” Comput. Methods Programs Biomed., vol. 158, pp. 71–91, 2018.[227] G. Pan, Z. Wu, and L. Sun, “Liveness detection for face recognition,” in Recent advances in face recognition, InTech, 2008.[228] J. Li, Y. Wang, T. Tan, and A. K. Jain, “Live face detection based on the analysis of fourier spectra,” in Biometric Technology for Human Identification, 2004, vol. 5404, pp. 296–304.[229] Y. A. U. Rehman, L. M. Po, and M. Liu, “LiveNet: Improving features generalization for face liveness detection using convolution neural networks,” Expert Syst. Appl., vol. 108, pp. 159–169, 2018.[230] W. Bao, H. Li, N. Li, and W. Jiang, “A liveness detection method for face recognition based on optical flow field,” in Image Analysis and Signal Processing, 2009. IASP 2009. International Conference on, 2009, pp. 233–236.[231] G. Pan, L. Sun, Z. Wu, and S. Lao, “Eyeblink-based anti-spoofing in face recognition from a generic webcamera,” in Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, 2007, pp. 1–8.[232] K. Kollreider, H. Fronthaler, and J. Bigun, “Non-intrusive liveness detection by face images,” Image Vis. Comput., vol. 27, no. 3, pp. 233–244, 2009.[233] Wikipedia, “List of writing systems,” https://en.wikipedia.org/wiki/List_of_writing_systems. .[234] G. Alvarez, B. Sheffer, and M. Bryant, “Offline Signature Verification with Convolutional Neural Networks,” 2016.