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A bibliometric analysis on deep learning during 2007–2019

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Abstract

As an emerging and applicable method, deep learning (DL) has attracted much attention in recent years. With the development of DL and the massive of publications and researches in this direction, a comprehensive analysis of DL is necessary. In this paper, from the perspective of bibliometrics, a comprehensive analysis of publications of DL is deployed from 2007 to 2019 (the first publication with keywords “deep learning” and “machine learning” was published in 2007). By preprocessing, 5722 publications are exported from Web of Science and they are imported into the professional science mapping tools: VOS viewer and Cite Space. Firstly, the publication structures are analyzed based on annual publications, and the publication of the most productive countries/regions, institutions and authors. Secondly, by the use of VOS viewer, the co-citation networks of countries/regions, institutions, authors and papers are depicted. The citation structure of them and the most influential of them are further analyzed. Thirdly, the cooperation networks of countries/regions, institutions and authors are illustrated by VOS viewer. Time-line review and citation burst detection of keywords are exported from Cite Space to detect the hotspots and research trend. Finally, some conclusions of this paper are given. This paper provides a preliminary knowledge of DL for researchers who are interested in this area, and also makes a conclusive and comprehensive analysis of DL for these who want to do further research on this area.

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References

  1. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507

    MathSciNet  MATH  Google Scholar 

  2. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

    MathSciNet  MATH  Google Scholar 

  3. LeCun Y, Bengio Y, Hinton GE (2015) Deep learning. Nature 521(7553):436–444

    Google Scholar 

  4. Guha R, Das N, Kundu M, Nasipuri M, Santosh KC DevNet: an efficient CNN architecture for handwritten Devanagari character recognition. Int J Pattern Recognit Artif Intell. https://doi.org/10.1142/S0218001420520096

  5. Mukherjee H, Ghosh S, Sen S, Md OS, Santosh KC, Phadikar S, Roy K (2019) Deep learning for spoken language identification: can we visualize speech signal patterns? Neural Comput Appl 31(12):8483–8501

    Google Scholar 

  6. Hao ZY (2019) Deep learning review and discussion of its future development. In MATEC Web of Conferences, EDP Sciences 277

  7. He KM, Zhang XY, Ren SQ, Sun J (2016) Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, p 770–778

  8. Ghosh S, Pal A, Jaiswal S, Santosh KC, Das N, Nasipuri M (2019) SegFast-V2: Semantic image segmentation with less parameters in deep learning for autonomous driving. Int J Mach Learn Cybern 10:3145–3154

    Google Scholar 

  9. Ghosh S, Shaw P, Das N, Santosh KC (2019) GSD-Net: compact network for pixel-level graphical symbol detection. In: International Conference on Document Analysis and Recognition Workshops (ICDARW), Sydney, Australia, 2019. p. 68–73

  10. Ghosh M, Mukherjee H, Obaidullah SM, Santosh KC, Das N, Roy K  (2019) Identifying the presence of graphical texts in scene images using CNN. International Conference on Document Analysis and Recognition Workshops (ICDARW), Sydney, Australia, 2019. p. 86–91

  11. Kamble PM, Hegadi RS (2017) Deep neural network for handwritten Marathi character recognition. Int J Image Robot 17(1):95–107

    Google Scholar 

  12. Ukil S, Ghosh S, Obaidullah SM, Santosh KC, Roy K, Das N (2020) Improved word-level handwritten Indic script identification by integrating small convolutional neural networks. Neural Comput Appl 32:2829–2844

    Google Scholar 

  13. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

  14. Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL, DeepLab (2017) Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848

    Google Scholar 

  15. Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G, Perersen S, Beattie C, Sadik A, Antonoglou I, King H, Kumaran D, Wierstra D, Legg S (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529–533

    Google Scholar 

  16. Taigman Y, Yang M, Ranzato MA, Wolf L (2014) DeepFace: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1701–1708

  17. Sawat DD, Hegadi RS (2017) Unconstrained face detection: a deep learning and machine learning combined approach. CSI Trans ICT 5(2):195–199

    Google Scholar 

  18. Sawat DD, Hegadi RS, Hegadi RS (2018) Eye like landmarks extraction and patching for face detection using deep neural network. In: International conference on recent trends in image processing and pattern recognition. Springer, Singapore

  19. Srinivasa Perumal R, Santosh KC, Chandra Mouli PVSSR (2019) Learning deep feature representation for face spoofing. In: Santosh K., Hegadi R (eds) Recent trends in image processing and pattern recognition. RTIP2R 2018. Communications in computer and information science, 1035. p. 178–185

  20. Ji SW, Xu W, Yang M, Yu K (2012) 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221–231

    Google Scholar 

  21. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sánchez (2017) C.I. A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

    Google Scholar 

  22. Guo GD, Zhang N (2019) A survey on deep learning based face recognition. Comput Vis Image Underst 189:102805

    Google Scholar 

  23. Ahmed KI, Tabassum H, Hossain E (2019) Deep learning for radio resource allocation in multi-cell networks. IEEE Network 33(6):188–195

    Google Scholar 

  24. Qin ZJ, Ye H, Li GY, Juang BH (2019) F. Deep learning in physical layer communications. IEEE Wirel Commun 26(2):93–99

    Google Scholar 

  25. Jiao LC, Zhang F, Liu F, Yang SY, Li LL, Feng ZX, Qu R (2019) A survey of deep learning-based object detection. IEEE Access 7:128837–128868

    Google Scholar 

  26. Khalil RA, Jones E, Babar MI, Jan T, Zafar MH, Alhussain T (2019) Speech emotion recognition using deep learning techniques: a review. IEEE Access 7:117327–117345

    Google Scholar 

  27. Zhou J, Huang JXJ, Chen Q, Hu QV, Wang TT, He L (2019) Deep learning for aspect-level sentiment classification: survey, vision, and challenges. IEEE Access 7:78454–78483

    Google Scholar 

  28. Li XF, Dong FW, Zhang S, Guo WB (2019) A survey on deep learning techniques in wireless signal recognition. Wireless Communications and Mobile Computing

  29. Shen DG, Wu GR, Suk HI (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221–248

    Google Scholar 

  30. Hamidinekoo A, Denton E, Rampun A, Honnor K, Zwiggelaar R (2018) Deep learning in mammography and breast histology, an overview and future trends. Med Image Anal 47:45–67

    Google Scholar 

  31. Monkam P, Qi SL, Ma H, Gao WM, Yao YD, Qian W (2019) Detection and classification of pulmonary nodules using convolutional neural networks: a survey. IEEE Access 7:78075–78091

    Google Scholar 

  32. Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR (2018) Deep learning for healthcare applications based on physiological signals: a review. Comput Methods Programs Biomed 161:1–13

    Google Scholar 

  33. He XR, Wu YY, Yu DJ, Merigó JM (2017) Exploring the ordered weighted averaging operator knowledge domain: a bibliometric analysis. Int J Intell Syst 32(11):1151–1166

    Google Scholar 

  34. White HD (2018) Pennants for Garfield: bibliometrics and document retrieval. Scientometrics 114(2):757–778

    Google Scholar 

  35. Laengle S, Merigó JM, Miranda J, Słowiński R, Bomze I, Borgonovo E, Dyson RG, Oliveira JF, Teunter R (2017) Forty years of the European Journal of Operational Research: a bibliometric overview. Eur J Oper Res 262(3):803–816

    MATH  Google Scholar 

  36. Yu DJ, Xu ZS, Kao YS, Lin CT (2017) The structure and citation landscape of IEEE Transactions on Fuzzy Systems (1994–2015). IEEE Trans Fuzzy Syst 26(2):430–442

    Google Scholar 

  37. Yu DJ, Xu ZS, Pedrycz W, Wang WR, Information (2017) Sciences 1968–2016: a retrospective analysis with text mining and bibliometric. Inf Sci 418:619–634

    Google Scholar 

  38. Cobo MJ, Martínez MA, Gutiérrez-Salcedo M, Fujita H (2015) Herrera-Viedma, E. 25 years at Knowledge-Based Systems: A bibliometric analysis. Knowl-Based Syst 80:3–13

    Google Scholar 

  39. Gu DX, Li JJ, Li XG, Liang CY (2017) Visualizing the knowledge structure and evolution of big data research in healthcare informatics. Int J Med Inf 98:22–32

    Google Scholar 

  40. Shi YH, Wang SM, Ma YQ, Macleod J, Chen M, Yang HH (2019) Research on the hotspots and trends of learning analytics based on citespace. In: International conference on blended learning 11546:239–248

  41. Dao SD, Abhary K, Marian R (2017) A bibliometric analysis of Genetic Algorithms throughout the history. Comput Ind Eng 110:395–403

    Google Scholar 

  42. Braun T (2005) Handbook of quantitative science and technology research: the use of publication and patent statistics in studies of S&T systems. Scientometrics 63(1):185–188

    MathSciNet  Google Scholar 

  43. Cobo MJ, López-Herrera AG, Herrera‐Viedma E, Herrera F (2011) Science mapping software tools: review, analysis, and cooperative study among tools. J Am Soc Inform Sci Technol 62(7):1382–1402

    Google Scholar 

  44. Van Eck NJ, Waltman L (2009) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84(2):523–538

    Google Scholar 

  45. Chen CM, CiteSpace II (2006) Detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Technol 57(3):359–377

  46. Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  47. Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

    Google Scholar 

  48. Jia YQ, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on Multimedia, pp 675–678

  49. Schmidhuber J (2015) Deep learning in neural networks: An overview. Neural networks 61:85–117

    Google Scholar 

  50. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster R (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410

    Google Scholar 

  51. Vedaldi A, Lenc K, Matconvnet (2015) Convolutional neural networks for matlab. In Proceedings of the 23rd ACM international conference on Multimedia, pp 689–692

  52. Cheng G, Zhou PC, Han JW (2016) Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Trans Geosci Remote Sens 54(12):7405–7415

    Google Scholar 

  53. Guo YM, Liu Y, Oerlemans A, Lao SY, Wu S, Lew MS (2016) Deep learning for visual understanding: a review. Neurocomputing 187:27–48

    Google Scholar 

  54. Ordóñez FJ, Roggen D (2016) Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1):115

    Google Scholar 

  55. Chuan PM, Son LH, Ali M, Khang TD, Huong LT, Dey N (2018) Link prediction in co-authorship networks based on hybrid content similarity metric. Appl Intell 48:2470–2486

    Google Scholar 

  56. Sengupta D (2020) Taxonomy on ambient computing: a research methodology perspective. Int J Ambient Comput Intell 11(1):1–33. https://doi.org/10.4018/IJACI.2020010101

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Business School, Sichuan University, Chengdu, 610064, China

    Yang Li, Zeshui Xu & Xinxin Wang

  2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China

    Xizhao Wang

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  1. Yang Li
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  4. Xizhao Wang
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Correspondence to Zeshui Xu.

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Li, Y., Xu, Z., Wang, X. et al. A bibliometric analysis on deep learning during 2007–2019. Int. J. Mach. Learn. & Cyber. 11, 2807–2826 (2020). https://doi.org/10.1007/s13042-020-01152-0

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  • DOI: https://doi.org/10.1007/s13042-020-01152-0

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