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A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning

Dargan, Shaveta, Kumar, Munish, Ayyagari, Maruthi Rohit, Kumar, Gulshan·2020·Archives of Computational Methods in Engineering
DOI 10.1007/s11831-019-09344-w
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Research works printed but not linked: 78

  1. Abadi M, Paul B, Jianmin C, Zhifeng C, Andy D, Jeffrey D, Matthieu D (2016) Tensorflow: a system for large-scale machine learning. In: The proceedings of the 12th USENIX symposium on operating systems design and implementation (OSDI’16), vol 16, pp 265–283
  2. Abbas Q, Ibrahim MEA, Jaffar MA (2018) A comprehensive review of recent advances on deep vision systems. Artif Intell Rev. https://doi.org/10.1007/s10462-018-9633-3 · DOI · Paper page
  3. Affonso C, Rossi ALD, Vieria FHA, Carvalho ACPDLFD (2017) Deep learning for biological image classification. Expert Syst Appl 85:114–122 · DOI · Paper page
  4. Alwzwazy HA, Albehadili HA, Alwan YS, Islam NE (2016) Handwritten digit recognition using convolutional neural networks. In: Proceedings of international journal of innovative research in computer and communication engineering, vol 4(2), pp 1101–1106
  5. Amato G, Carrara F, Falchi F, Gennaro C, Meghini C, Vairo C (2017) Deep learning for decentralized parking lot occupancy detection. Expert Syst Appl 72:327–334 · DOI · Paper page
  6. Araque O, Corcuera-Platas I, Sánchez-Rada JF, Iglesias CA (2017) Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Syst Appl 77:236–246 · DOI · Paper page
  7. Ashiquzzaman A, Tushar AK (2017) Handwritten arabic numeral recognition using deep learning neural networks. In: Proceedings of IEEE international conference on imaging, vision & pattern recognition, pp 1–4. https://doi.org/10.1109/ICIVPR.2017.7890866 · DOI
  8. Azar MY, Hamey L (2017) Text summarization using unsupervised deep learning. Expert Syst Appl 68:93–105 · DOI · Paper page
  9. Chen XW, Lin X (2014) Big data deep learning: challenges and perspectives. IEEE 2:514–525. https://doi.org/10.1109/ACCESS.2014.2325029 · DOI · Paper page
  10. Chen CH, Lee CR, Lu WCH (2016) A mobile cloud framework for deep learning and its application to smart car camera. In: Proceedings of the international conference on internet of vehicles, pp 14–25. https://doi.org/10.1007/978-3-319-51969-22 · doi:10.1007/978-3-319-51969-22
  11. Cheng D, Gong Y, Changb X, Shia W, Hauptmannb A, Zhenga N (2018) Deep feature learning via structured graph Laplacian embedding for person re-identification. Pattern Recogn 82:94–104 · DOI
  12. Chu J, Srihari S (2014) Writer identification using a deep neural network. In: Proceedings of the 2014 Indian conference on computer vision graphics and image processing, pp 1–7 · doi:10.1145/2683483.2683514
  13. Dai Y, Wang G (2018) A deep inference learning framework for healthcare. Pattern Recogn Lett. https://doi.org/10.1016/j.patrec.2018.02.009 · DOI
  14. Dhieb T, Ouarda W, Boubaker H, Alilmi AM (2016) Deep neural network for online writer identification using Beta-elliptic model. In: Proceedings of the international joint conference on neural networks, pp 1863–1870 · doi:10.1109/ijcnn.2016.7727426
  15. Falcini F, Lami G, Costanza AM (2017) Deep learning in automotive software. IEEE Softw 34(3):56–63. https://doi.org/10.1109/MS.2017.79 · DOI
  16. Gheisari M, Wang G, Bhuiyan MZA (2017) A survey on deep learning in big data. In: Proceedings of the IEEE international conference on embedded and ubiquitous computing (EUC), pp 1–8 · DOI
  17. Ghosh MMA, Maghari AY (2017) A comparative study on handwriting digit recognition using neural networks. In: Proceedings of the promising electronic technologies (ICPET), pp 77–81
  18. Gurjar N, Sudholt S, Fink GA (2018) Learning deep representations for word spotting under weak supervision. In: Proceedings of the 13th IAPR international workshop on document analysis systems (DAS), pp 7s–12s · doi:10.1109/das.2018.35
  19. Hamid OA, Jiang H (2013) Rapid and effective speaker adaptation of convolutional neural network based models for speech recognition. In: INTERSPEECH, pp 1248–1252
  20. Ignatov A (2018) Real-time human activity recognition from accelerometer data using convolutional neural networks. Appl Soft Comput 62:915–922 · DOI · Paper page
  21. Jia X (2017) image recognition method based on deep learning. In: Proceedings of the 29th IEEE, Chinese control and decision conference (CCDC), pp 4730–4735 · DOI
  22. Kaushal M, Khehra B, Sharma A (2018) Soft computing based object detection and tracking approaches: state-of-the-art survey. Appl Soft Comput 70:423–464 · DOI
  23. Krishnan P, Dutta K, Jawahar CV (2018) Word spotting and recognition using deep embedding. In: Proceedings of 13th IAPR international workshop on document analysis systems (DAS). https://doi.org/10.1109/das.2018.70 · DOI
  24. Lee SH, Chan CS, Mayo SJ, Remagnino P (2017) How deep learning extracts and learns leaf features for plant classification. Pattern Recogn 71:1–13 · DOI · Paper page
  25. Ling ZH, Kang SY, Zen H, Senior A, Schuster M, Qian XJ, Meng HM, Deng L (2015) Deep learning for acoustic modeling in parametric speech generation: a systematic review of existing techniques and future trends. IEEE Signal Process Mag 32(3):35–52 · DOI · Paper page
  26. Ling Y, Jin C, Guoru D, Ya T, Jian Y, Jiachen S (2018) Spectrum prediction based on Taguchi method in deep learning with long short-term memory. IEEE Access 6(1):45923–45933
  27. Liu PH, Su SF, Chen MC, Hsiao CC (2015) Deep learning and its application to general image classification. In: Proceedings of the international conference on informatics and cybernetics for computational social systems, pp 1–4 · DOI
  28. Looks M, Herreshoff M, Hutchins D, Norvig P (2017) Deep learning with dynamic computation graphs. In: Proceedings of the international conference on learning representation, pp 1–12
  29. Makhmudov AZ, Abdukarimov SS (2016) Speech recognition using deep learning algorithms. In: Proceedings of the international conference on informatics: problems, methodology, technologies, pp 10–15
  30. Markovnikov N, Kipyatkova I, Karpov A, Filchenkov A (2018) Deep neural networks in russian speech recognition. Artif Intell Nat Lang Commun Comput Inf Sci 789:54–67. https://doi.org/10.1007/978-3-319-71746-3_5 · DOI
  31. Mohamed A, Dahl G, Geoffrey H (2009) Deep belief networks for phone recognition. In: Proceedings of the nips workshop on deep learning for speech recognition and related applications, pp 1–9
  32. Mohsen AM, El-Makky NM, Ghanem N (2017) Author identification using deep learning. In: Proceedings of the 15th IEEE international conference on machine learning and applications, pp 898–903
  33. Nguyen HD, Le AD, Nakagawa M (2015) Deep neural networks for recognizing online handwritten mathematical symbols. In: Proceedings of the 3rd IAPR IEEE Asian conference on pattern recognition (ACPR), pp 121–125 · DOI
  34. Noda K, Yamaguchi Y, Nakadai K, Okuno HG, Ogata T (2015) Audio-visual speech recognition using deep learning. Appl Intell 42(4):722–737 · DOI · Paper page
  35. Poznanski A, Wolf L (2016) CNN-N-gram for handwriting word recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2305–2314
  36. Prabhanjan S, Dinesh R (2017) deep learning approach for devanagari script recognition. Proc Int J Image Graph 17(3):1750016. https://doi.org/10.1142/S0219467817500164 · DOI
  37. Puthussery AR, Haradi KP, Erol BA, Benavidez P, Rad P, Jamshidi M (2017) A deep vision landmark framework for robot navigation. In: Proceedings of the system of systems engineering conference, pp 1–6 · DOI
  38. Razzak MI, Naz S, Zaib A (2018) Deep learning for medical image processing: overview, challenges and the future. Classif BioApps Lect Notes Comput Vis Biomech 26:323–350
  39. Ripoll VJR, Wojdel A, Romero A, Ramos P, Brugada J (2016) ECG assessment based on neural networks with pertaining. Appl Soft Comput 49:399–406 · DOI
  40. Rudin F, Li GJ, Wang K (2017) An algorithm for power system fault analysis based on convolutional deep learning neural networks. Int J Res Educ Sci Methods 5(9):11–18
  41. Salazar F, Toledo MA, González JM, Oñate E (2012) Early detection of anomalies in dam performance: a methodology based on boosted regression trees. Struct Control Health Monit 24(11):2012–2017 · DOI
  42. Salazar F, Toledo MA, Morán R, Oñate E (2015) An empirical comparison of machine learning techniques for dam behaviour modelling structural safety. Struct Saf 56:9–17 · DOI · Paper page
  43. Salazar F, Toledo MA, Oñate E, Suárez B (2016) Interpretation of dam deformation and leakage with boosted regression trees. Eng Struct 119:230–251 · doi:10.1016/j.engstruct.2016.04.012
  44. Salazar F, Moran R, Toledo MA, Oñate E (2017) Data-based models for the prediction of dam behaviour: a review and some methodological considerations. Arch Comput Methods Eng 24(1):1–21 · DOI · Paper page
  45. Sanakoyeu A, Bautista MA, Ommer B (2018) Deep unsupervised learning of visual similarities. Pattern Recogn 78:331–343 · DOI
  46. Santana LMQD, Santos RM, Matos LN, Macedo HT (2018) Deep neural networks for acoustic modeling in the presence of noise. IEEE Latin Am Trans 16(3):918–925 · DOI
  47. Serizel RGD (2016) Deep-neural network approaches for speech recognition with heterogeneous groups of speakers including children. Nat Lang Eng 1(3):1–26
  48. Soniya, Paul S, Singh L (2015) A review on advances in deep learning. In: Proceedings of IEEE workshop on computational intelligence: theories, applications and future directions (WCI), pp 1–6. https://doi.org/10.1109/wci.2015.7495514 · DOI
  49. Sudholt S, Fink GA (2017) Attribute CNNs for word spotting in handwritten documents. Int J Doc Anal Recognit (IJDAR). https://doi.org/10.1007/s10032-018-0295-0 · DOI
  50. Thomas S, Chatelain C, Heutte L, Paquet T, Kessentini Y (2015) A deep HMM model for multiple keywords spotting in handwritten documents. Pattern Anal Appl 18(4):1003–1015 · DOI
  51. Ucar A, Demir Y, Guzelis C (2017) Object recognition and detection with deep learning for autonomous driving applications. Int Trans Soc Model Simul 93(9):759–769 · DOI · Paper page
  52. Vasconcelos CN, Vasconcwlos BN (2017) Experiment using deep learning for dermoscopy image analysis. Pattern Recognit Lett. https://doi.org/10.1016/j.patrec.2017.11.005 · DOI
  53. Wang Y, Liu M, Bao Z (2016) Deep learning neural network for power system fault diagnosis. In: Proceedings of the 35th Chinese control conference, 1–6 · DOI
  54. Wang T, Wen CK, Wang H, Gao F, Jiang F, Jin S (2017) Deep learning for wireless physical layer: opportunities and challenges. China Commun 14(11):92–111 · DOI · Paper page
  55. Wicht B, Fischer A, Hennebert J (2016) Deep learning features for handwritten keyword spotting. In: Proceedings of the 23rd international conference on pattern recognition (ICPR). https://doi.org/10.1109/icpr.2016.7900165 · doi:10.1109/icpr.2016.7900165
  56. Wu Z, Swietojanski P, Veaux C, Renals S, King S (2015) A study of speaker adaptation for DNN-based speech synthesis. In: Proceedings of the sixteenth annual conference of the international speech communication association, pp 879–883 · DOI
  57. Xiao B, Xiong J, Shi Y (2016) Novel applications of deep learning hidden features for adaptive testing. In: Proceedings of the 21st Asia and South Pacific design automation conference, pp 743–748 · doi:10.1109/aspdac.2016.7428100
  58. Xue S, Hamid OA, Jiang H, Dai L, Liu Q (2014) Fast adaptation of deep neural network based on discriminant codes for speech recognition. IEEE/ACM Trans Audio Speech Lang Process 22(12):1713–1725 · DOI
  59. Yadav U, Verma S, Xaxa DK, Mahobiya C (2017) A deep learning based character recognition system from multimedia document. In: Proceedings of the international conference on innovations in power and advanced computing technologies, pp 1–7 · doi:10.1109/ipact.2017.8245200
  60. Yonel B, Mason E, Yazici B (2017) Deep learning for passive synthetic aperture radar. IEEE J Sel Top Signal Process 12(1):90–103 · DOI · Paper page
  61. Yu X, Wu X, Luo C, Ren P (2017) Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework. GIScience & Remote Sens: 1–19 · DOI · Paper page
  62. Yuan X, Xie L, Abouelenien M (2018) A regularized ensemble framework of deep learning for cancer detection from multi-class, imbalanced training data. Pattern Recogn 77:160–172 · DOI · Paper page
  63. Zhang L, Zhang L, Du B (2016) Deep learning for remote sensing data: a technical tutorial on the state of the art. IEEE Geosci Remote Sens Mag 4(2):22–40 · DOI · Paper page
  64. Zhao C, Chen K, Wei Z, Chen Y, Miao D, Wang W (2018) Multilevel triplet deep learning model for person reidentification. Pattern Recogn Lett. https://doi.org/10.1016/j.patrec.2018.04.029 · DOI
  65. Zhong SH, Li Y, Le B (2015) Query oriented unsupervised multi document summarization via deep learning. Expert Syst Appl, pp 1–10 · DOI
  66. Zhou X, Gong W, Fu W, Du F (2017) Application of deep learning in object detection. In: Proceedings of the IEEE/ACIS 16th international conference on computer and information science (ICIS), pp 631–634 · DOI
  67. Zulkarneev M, Grigoryan R, Shamraev N (2013) Acoustic modeling with deep belief networks for Russian speech recognition. In: Proceedings of the international conference on speech and computer, pp 17–24 · DOI
  68. Chandra B, Sharma RK (2016) Deep learning withadaptive learning rate using Laplacian score, expert systems with applications. Int J 63(C):1–7
  69. Wu Z, Swietozanski P, Veaux C, Renals S (2015) A study of speaker adaptation for DNN-based speech synthesis. In: Proceedings of the interspeech conference, pp 1–5 · DOI
  70. Xing L, Qiao Y (2016) DeepWriter: a multi-stream deep CNN for text-independent writer identification. Comput Vis Pattern Recognit. arXiv:1606.06472 · DOI
  71. Roy P, Bhunia AK, Das A, Dey P (2016) HMM-based indic handwritten word recognition using zone segmentation. Pattern Recognit 60:1057–1075. https://doi.org/10.1016/j.patcog.2016.04.012 · DOI
  72. Loh BCS, Then PHH (2017) Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions. M Health. https://doi.org/10.21037/mhealth.2017.09.01 · DOI
  73. Cireşan D, Meier U, Schmidhuber J (2012) Multi-column deep neural networks for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3642–3649 · DOI · Paper page
  74. Ota K, Dao MS, Mezaris V, Natale FGBD (2017) Deep learning for mobile multimedia: a survey. ACM Trans Multimed Comput Commun Appl (TOMM) (TOMM) 13(3s):34
  75. Wang L, Sng D (2015) Deep learning algorithms with applications to video analytics for a smart city: a survey. arXiv, preprint arXiv: 1512.03131 · arXiv
  76. Papakostas M, Giannakopoulos T (2018) Speech-music discrimination using deep visual feature extractors. Expert Syst Appl 114:334–344 · DOI
  77. Arevalo A, Niño J, Hernández G, Sandoval J (2016) High-frequency trading strategy based on deep neural networks. In: Proceedings of the international conference on intelligent computing, pp 424–436 · DOI
  78. Zhang XL, Wu J (2013) Denoising deep neural networks based voice activity detection. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing, pp 853–857

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Metadata

Authors
Dargan, S., Kumar, M., Ayyagari, M. R., & Kumar, G.
Venue
Archives of Computational Methods in Engineering
Publisher
Springer Science and Business Media LLC
Published
2019-06-01
Volume
27
Issue
4
Pages
1071-1092
ISSN
  • 1134-3060 (ISSN-L)
  • 1886-1784
DOI
10.1007/s11831-019-09344-w
OpenAlex
W2947411064
Cited by
1,198
References
6
FWCI
46.62
Type
journal article
Language
en
Open access
closed
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Dargan, S., Kumar, M., Ayyagari, M. R., & Kumar, G. (2020). A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning. Archives of Computational Methods in Engineering. https://doi.org/10.1007/s11831-019-09344-w

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