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Deep Learning: Hope or Hype

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Abstract

In this paper, we investigate the literature around deep learning to identify its usefulness in different application domains. Our paper identifies that the effectiveness of deep learning is highly visible in the medical imaging area. Other application domains are yet to make any significant progress using deep learning. Therefore, we conclude that deep learning is a good solution for medical imaging analysis. However, its benefits are yet to be realized in other domains and researchers are pursuing to explore its effectiveness to solve problems in these domains. Our initial critical evaluation suggests that deep learning may be a hype in most domains. In order to probe this further, we call for a deeper engagement with prior literature in different application domains of deep learning.

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Notes

  1. https://www.technologyreview.com/s/608911/is-ai-riding-a-one-trick-pony/.

  2. 1 ZB = 1 Trillion Gigabytes.

References

  1. Pouyanfar S, Sadiq S, Yan Y, Tian H, Tao Y, Reyes MP, Shyu M-L, Chen S-C, Iyengar SS (2018) A survey on deep learning: algorithms, techniques, and applications. ACM Comput. Surv. 51(5):92:1–92:36

    Google Scholar 

  2. Ahmed M, Pathan ASK (2018) Investigating deep learning for collective anomaly detection—an experimental study. In: Sixth international symposium on security in computing and communications (SSCC 2018), vol 969

  3. Ahmed M (2018) Collective anomaly detection techniques for network traffic analysis. Ann Data Sci 5(4):497–512

    Article  Google Scholar 

  4. Ahmed M, Mahmood AN, Hu J (2016) A survey of network anomaly detection techniques. J Netw Comput Appl 60:19–31

    Article  Google Scholar 

  5. Shah SH, Yaqoob I (2016) A survey: Internet of Things (IoT) technologies, applications and challenges. IEEE Smart Energy Grid Eng (SEGE) 2016:381–385

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. Hatcher WG, Yu W (2018) A survey of deep learning: platforms, applications and emerging research trends. IEEE Access 6:24411–24432

    Article  Google Scholar 

  8. Xu B, Liu J, Hou X, Liu B, Garibaldi J, Ellis IO, Green A, Shen L, Qiu G (2019) Look, investigate, and classify: a deep hybrid attention method for breast cancer classification. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019), pp 914–918

  9. Yu M, Wu J (2019) CEAM: a novel approach using cycle embeddings with attention mechanism for stock price prediction. In: 2019 IEEE international conference on big data and smart computing (BigComp), pp 1–4

  10. Ruan X, Ren D, Zhu X, Huang J (2019) Mobile robot navigation based on deep reinforcement learning. In: 2019 Chinese control and decision conference (CCDC), pp 6174–6178

  11. Tan R, Zhou J, Du H, Shang S, Dai L (2019) A modeling processing method for video games based on deep reinforcement learning. In: 2019 IEEE 8th joint international information technology and artificial intelligence conference (ITAIC), pp 939–942

  12. Kamaldinov I, Makarov I (2019) Deep reinforcement learning in match-3 game. In: 2019 IEEE conference on games (CoG), pp 1–4

  13. Gan Y, Yang T, He C (2014) A deep graph embedding network model for face recognition. In: 2014 12th international conference on signal processing (ICSP), pp 1268–1271

  14. Jiang H, Turki T, Wang JTL (2018) DLGraph: Malware detection using deep learning and graph embedding. In: 2018 17th IEEE international conference on machine learning and applications (ICMLA), pp 1029–1033

  15. Zhang Q, Yang LT, Chen Z, Li P (2018) A survey on deep learning for big data. Inf Fus 42:146–157

    Article  Google Scholar 

  16. Gheisari M, Wang G, Bhuiyan MZA (2017) A survey on deep learning in big data. In: 2017 IEEE international conference on computational science and engineering (CSE) and IEEE international conference on embedded and ubiquitous computing (EUC) 2:173–180

  17. Druzhkov PN, Kustikova VD (2016) A survey of deep learning methods and software tools for image classification and object detection. Pattern Recognit Image Anal 26(1):9–15

    Article  Google Scholar 

  18. Ghosh P (2019) AAAS: Machine learning ’causing science crisis’. https://www.bbc.com/news/

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Authors and Affiliations

  1. Academic Centre of Cyber Security Excellence, School of Science, Edith Cowan University, Joondalup, WA, 6027, Australia

    Mohiuddin Ahmed

  2. Department of Future Technologies, University of Turku, 20500, Turku, Finland

    A. K. M. Najmul Islam

Authors
  1. Mohiuddin Ahmed
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  2. A. K. M. Najmul Islam
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Correspondence to Mohiuddin Ahmed.

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Ahmed, M., Najmul Islam, A.K.M. Deep Learning: Hope or Hype. Ann. Data. Sci. 7, 427–432 (2020). https://doi.org/10.1007/s40745-019-00237-0

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