Skip to main content

Advertisement

Log in

An Extensive Study on Deep Learning: Techniques, Applications

  • Original Paper
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

Abstract

Deep learning is associate degree future field of machine learning (ML) analysis. It consists of variety of numerous concealed layers of artificial neural networks ANN). Deep learning methods applies nonlinear transformation and high-level model abstraction to giant databases. Recent advances in deep learning design among several fields have already contributed considerably to computer science. This text present class study on commitments and novel uses of escalated instruction of intensive education. The subsequent examination presents however and during which key application intensive algorithms for learning are used. Additionally, deep learning methodology is given with enhancements and its hierarchy in linear and non-linear functions and compared with a lot of ancient algorithms in widespread applications. The status of the survey of art provides a common rundown on the novel thought and therefore rising learning and deep learning quality.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Fourie C (2003) Deep learning? What deep learning? South Afr J High Educ. https://doi.org/10.4314/sajhe.v17i1.25201

    Article  Google Scholar 

  2. Pandey H (2017) Genetic algorithm for grammar induction and rules verification through a PDA simulator. IAES Int J Artif Intell (IJ-AI) 6(3):100. https://doi.org/10.11591/ijai.v6.i3.pp100-111

    Article  Google Scholar 

  3. Ordonez C, Zhang Y, Johnsson S (2018) Scalable machine learning computing a data summarization matrix with a parallel array DBMS. Distrib Parallel Databases 37(3):329–350. https://doi.org/10.1007/s10619-018-7229-1

    Article  Google Scholar 

  4. Criminisi A (2016) Machine learning for medical images analysis. Med Image Anal 33:91–93. https://doi.org/10.1016/j.media.2016.06.002

    Article  Google Scholar 

  5. Goel A (2017) Editorial: expository AI applications. AI Mag 38(1):3. https://doi.org/10.1609/aimag.v38i1.2739

    Article  Google Scholar 

  6. Alonso E (2002) AI and agents: state of the art. AI Mag 23(3):25–25

    Google Scholar 

  7. Shapiro, S. C. (1971). The MIND system: a data structure for semantic information processing (Vol. 837, No. PR). Rand Corp Santa Monica Califsss

  8. Beck J, Stern M, Haugsjaa E (1996) Applications of AI in education. XRDS Crossroads ACM Mag Stud 3(1):11–15

    Google Scholar 

  9. Faught WS (1986) Applications of AI in engineering. Computer 7:17–27

    Google Scholar 

  10. Zhenan S, Zhaoxiang Z, Wei W, Fei L, Tieniu T (2020) Artificial intelligence: developments and advances in 2019. Front Data Comput 1(2):1–16

    Google Scholar 

  11. Li XH, Cao CC, Shi Y, Bai W, Gao H, Qiu L, Chen L (2020) A survey of data-driven and Knowledge-aware eXplainable AI. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2020.2983930

    Article  Google Scholar 

  12. Maxwell AE, Warner TA, Fang F (2018) Implementation of machine-learning classification in remote sensing: an applied review. Int J Remote Sens 39(9):2784–2817

    Google Scholar 

  13. Usama M, Qadir J, Raza A, Arif H, Yau KLA, Elkhatib Y, Al-Fuqaha A (2019) Unsupervised machine learning for networking: techniques, applications and research challenges. IEEE Access 7:65579–65615

    Google Scholar 

  14. Osband, I., Doron, Y., Hessel, M., Aslanides, J., Sezener, E., Saraiva, A., & Van Roy, B. (2019). Behaviour suite for reinforcement learning. arXiv preprint

  15. Mnih, V., Badia, A. P., Graves, A. B., Harley, T.J . A., Silver, D., & Kavukcuoglu, K. (2019). U.S. Patent Application No. 16/403,388.

  16. Mnih, V., Czarnecki, W., Jaderberg, M. E., Schaul, T., Silver, D., & Kavukcuoglu, K. (2019). U.S. Patent Application No. 16/403,385.

  17. Mnih, V., Czarnecki, W., Jaderberg, M. E., Schaul, T., Silver, D., & Kavukcuoglu, K. (2019). U.S. Patent Application No. 16/403,385

  18. Zhai, X., Oliver, A., Kolesnikov, A., & Beyer, L. (2019). S4l: self-supervised semi-supervised learning. In: proceedings of the IEEE international conference on computer vision (pp. 1476–1485)

  19. Souza JTD, Francisco ACD, Piekarski CM, Prado GFD (2019) Data mining and machine learning to promote smart cities: a systematic review from 2000 to 2018. Sustainability 11(4):1077

    Google Scholar 

  20. Niculescu V (2019) High performance computing in big data analytics. Appl Med Inform 41:2–21

    Google Scholar 

  21. Zhao ZQ, Zheng P, Xu ST, Wu X (2019) Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst 30(11):3212–3232

    Google Scholar 

  22. Huang, S. L., Xu, X., Zheng, L., & Wornell, G. W. (2019). An information theoretic interpretation to deep neural networks. In: 2019 IEEE international symposium on information theory (ISIT) (pp. 1984–1988). IEEE

  23. Elfwing S, Uchibe E, Doya K (2018) Sigmoid-weighted linear units for neural network function approximation in reinforcementlearning. Neural Netw 107:3–11

    Google Scholar 

  24. Szegedy, C., Erhan, D., & Toshev, A. T. (2016). U.S. Patent No. 9,275,308. Washington, DC: U.S. Patent and Trademark Office

  25. Kombrink, Stefan & Mikolov, Tomas & Karafiát, Martin & Burget, Lukas. (2011). Recurrent neural network based language modeling in meeting recognition. In: proceedings of the annual conference of the international speech communication association, INTERSPEECH. 2877–2880

  26. Hinton G (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29(6):82–97

    Google Scholar 

  27. Chung, J., Ahn, S., & Bengio, Y. (2016). Hierarchical multiscale recurrent neural networks. arXiv preprint

  28. Li, B., & Sainath, T. N. (2018). U.S. Patent No. 9,984,683. Washington, DC: U.S. Patent and Trademark Office

  29. Martin, L. J., Ammanabrolu, P., Wang, X., Hancock, W., Singh, S., Harrison, B., & Riedl, M.O. (2018). Event representations for automated story generation with deep neuralnets. In: thirty-second AAAI conference on artificial intelligence

  30. Ren S, He K, Girshick R, Zhang X, Sun J (2016) Object detection networks on convolutional feature maps. IEEE Trans Pattern Anal Mach Intell 39(7):1476–1481

    Google Scholar 

  31. Miyato T, Maeda SI, Koyama M, Ishii S (2018) Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Trans Pattern Anal Mach Intell 41(8):1979–1993

    Google Scholar 

  32. Wang M, Fu W, Hao S, Tao D, Wu X (2016) Scalable semi-supervised learning by efficient anchor graph regularization. IEEE Trans Knowl Data Eng 28(7):1864–1877

    Google Scholar 

  33. Gong C, Tao D, Maybank SJ, Liu W, Kang G, Yang J (2016) Multi-modal curriculum learning for semi-supervised image classification. IEEE Trans Image Process 25(7):3249–3260

    MathSciNet  MATH  Google Scholar 

  34. Tang, H., Xu, D., Yan, Y., Corso, J. J., Torr, P. H., & Sebe, N. (2020). Multi-channel attention selection GANs for guided image-to-image translation. arXiv preprint

  35. Royer A, Bousmalis K, Gouws S, Bertsch F, Mosseri I, Cole F, Murphy K (2020) Xgan: unsupervised image-to-image translation for many-to-many mappings. In Domain adaptation for visual understanding. Springer, Cham, pp. 33–49

  36. French, G., Mackiewicz, M., & Fisher, M. (2017). Self-ensembling for visual domain adaptation. arXiv preprint

  37. Saito, K., Ushiku, Y., & Harada, T. (2017). Asymmetric tri-training for unsupervised domain adaptation. In: proceedings of the 34th international conference on machine learning-volume 70 (pp. 2988–2997). JMLR. org

  38. Hung, W. C., Tsai, Y. H., Liou, Y. T., Lin, Y. Y., & Yang, M. H. (2018). Adversarial learning for semi-supervised semantic segmentation. arXiv preprint

  39. Van Hasselt, H., Guez, A., & Silver, D. (2016). Deep reinforcement learning with double q-learning. In: thirtieth AAAI conference on artificial intelligence

  40. M. Hausknecht and P. Stone (2015). Deep recurrent q-learning for partially observable mdps. AAAI

  41. Hessel, M., Modayil, J., Van Hasselt, H., Schaul, T., Ostrovski, G., Dabney, W., & Silver, D. (2018). Rainbow: combining improvements in deep reinforcement learning. In thirty-second AAAI conference on artificial intelligence

  42. Wu, Y., Mansimov, E., Grosse, R. B., Liao, S., & Ba, J. (2017). Scalable trust-region method for deep reinforcement learning using kronecker-factored approximation. In: advances in neural information processing systems (pp. 5279–5288)

  43. Dabney, W., Rowland, M., Bellemare, M. G., & Munos, R. (2018, April). Distributional reinforcement learning with quantile regression. In: thirty-second AAAI conference on artificial intelligence

  44. Marcus, G. (2018). Deep learning: a critical appraisal. arXiv preprint

  45. Shu, M. (2019). Deep learning for image classification on very small datasets using transfer learning

  46. Jin L, Li S, La HM, Luo X (2017) Manipulability optimization of redundant manipulators using dynamic neural networks. IEEE Trans Ind Electron 64(6):4710–4720

    Google Scholar 

  47. Bertinetto, L., Henriques, J. F., Valmadre, J., Torr, P., & Vedaldi, A. (2016). Learning feed-forward one-shot learners. In: advances in neural information processing systems (pp. 523–531)

  48. SaishanmugaRaja V, Rajagopalan SP (2013) IRIS recognition system using neural network and genetic algorithm. Int J Comput Appl 68(20):49–53. https://doi.org/10.5120/11699-7431

    Article  Google Scholar 

  49. Tran, D. T., Kanniainen, J., Gabbouj, M., & Iosifidis, A. (2019). Data-driven neural architecture learning for financial time-series forecasting. arXiv preprint

  50. Qi H, Shi Y, Tian Y, Mayhew C, Yu DL, Gomm JB, Zhang Q (2019) A new fault diagnosis and fault-tolerant control method for mechanical and aeronautical systems with neural estimators. Adv Mech Eng 11(11):1687814019891659

    Google Scholar 

  51. Huang, Y., Zhang, Y., & Yan, C. (2019). Automatic sleep staging based on deep neural network using single channel EEG. In: international conference on knowledge management in organizations (pp. 63–73). Springer: Cham

  52. Martens J, & Sutskever I (2011). Learning recurrent neural networks with hessian-free optimization. In: proceedings of the 28th international conference on machine learning (ICML-11) (pp. 1033–1040)

  53. Tolosana R, Vera-Rodriguez R, Fierrez J, Ortega-Garcia J (2018) Exploring recurrent neural networks for on-line handwritten signature biometrics. IEEE Access 6:5128–5138

    Google Scholar 

  54. Kasongo SM, Sun Y (2019) A deep long short-term memory based classifier for wireless intrusion detection system. ICT Express 6(2):98–103

    Google Scholar 

  55. Bouktif S, Fiaz A, Ouni A, Serhani MA (2020) Multi-sequence LSTM-RNN deep learning and metaheuristics for electric load forecasting. Energies 13(2):391

    Google Scholar 

  56. Zhang YF, Fitch P, Thorburn PJ (2020) Predicting the trend of dissolved oxygen based on the kPCA-RNN model. Water 12(2):585

    Google Scholar 

  57. Razzak, F., Yi, F., Yang, Y., & Xiong, H. (2019). An integrated multimodal attention-based approach for bank stress test prediction. In: 2019 IEEE international conference on data mining (ICDM) (pp. 1282–1287). IEEE

  58. Kumar, P., Gupta, M., Gupta, M., & Sharma, A. (2019). Profession Identification Using Handwritten Text Images. In: international conference on computer vision and image processing (pp. 25–35). Springer: Singapore

  59. Yan LC, Bengio YS, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Google Scholar 

  60. Tong W, Li L, Zhou X, Hamilton A, Zhang K (2019) Deep learning PM 2.5 concentrations with bidirectional LSTM RNN. Air Qual Atmos Health 12(4):411–423

    Google Scholar 

  61. Vedaldi A, & Lenc K (2015). Matconvnet: Convolutional neural networks for matlab. In: proceedings of the 23rd ACM international conference on multimedia (pp. 689–692)

  62. Aloysius N, Geetha M (2017). A review on deep convolutional neural networks. In: 2017 international conference on communication and signal processing (ICCSP) (pp. 0588–0592). IEEE

  63. Chi YS, Kamarulzaman SF (2020) Intelligent gender recognition system for classification of gender in malaysian demographic. In: Nasir ANK, Ahmad MA, Najib MS (eds) In ECCE 2019. Springer, Singapore, pp 283–295

    Google Scholar 

  64. Wang C, Zhang Z, Zhou X (2018) An image copy-move forgery detection scheme based on A-KAZE and SURF features. Symmetry 10(12):706

    MATH  Google Scholar 

  65. Gan W, Wang S, Lei X, Lee MS, Kuo CCJ (2018) Online CNN-based multiple object tracking with enhanced model updates and identity association. Signal Process Image Commun 66:95–102

    Google Scholar 

  66. Kong X, Gong S, Su L, Howard N, Kong Y (2018) Automatic detection of acromegaly from facial photographs using machine learning methods. EBioMedicine 27:94–102

    Google Scholar 

  67. Telgarsky, M. (2015). Representation benefits of deep feedforward networks. arXiv preprint

  68. Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26

    Google Scholar 

  69. Yan, R., Song, Y., & Wu, H. (2016) Learning to respond with deep neural networks for retrieval-based human-computer conversation system. In: proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval (pp. 55–64)

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

    Google Scholar 

  71. Milz, S., Arbeiter, G., Witt, C., Abdallah, B., & Yogamani, S. (2018). Visual slam for automated driving: Exploring the applications of deep learning. In: proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 247–257)

  72. Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R, Muharemagic E (2015) Deep learning applications and challenges in big data analytics. J Big Data 2(1):1

    Google Scholar 

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

    Google Scholar 

  74. Zhang K, Zhu Y, Leng S, He Y, Maharjan S, Zhang Y (2019) Deep learning empowered task offloading for mobile edge computing in urban informatics. IEEE Internet Things J 6(5):7635–7647

    Google Scholar 

  75. Lemley J, Bazrafkan S, Corcoran P (2017) Deep learning for consumer devices and services: pushing the limits for machine learning, artificial intelligence, and computer vision. IEEE Consum Electron Mag 6(2):48–56

    Google Scholar 

  76. Wu, R., Yan, S., Shan, Y., Dang, Q., & Sun, G. (2015). Deep image: Scaling up image recognition. arXiv preprint

  77. Noda K, Yamaguchi Y, Nakadai K, Okuno HG, Ogata T (2015) Audio-visual speech recognition using deep learning. Appl Intell 42(4):722–737

    Google Scholar 

  78. Deng L, Liu Y (eds) (2018) Deep learning in natural language processing. Springer, Singapore

    Google Scholar 

  79. Sun S, Luo C, Chen J (2017) A review of natural language processing techniques for opinion mining systems. Inf Fusion 36:10–25

    Google Scholar 

  80. Min S, Lee B, Yoon S (2017) Deep learning in bioinformatics. Brief Bioinform 18(5):851–869

    Google Scholar 

  81. Ker J, Wang L, Rao J, Lim T (2017) Deep learning applications in medical image analysis. IEEE Access 6:9375–9389

    Google Scholar 

  82. Nakao A, Du P (2018) Toward in-network deep machine learning for identifying mobile applications and enabling application specific network slicing. IEICE Trans Commun. https://doi.org/10.1587/transcom.2017CQI0002

    Article  Google Scholar 

  83. Kumar, D., Kumar, C., & Shao, M. (2017). Cross-database mammographic image analysis through unsupervised domain adaptation. In: 2017 IEEE international conference on big data (Big Data) (pp. 4035–4042). IEEE

  84. Wang Y, Xu W (2018) Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud. Decis Support Syst 105:87–95

    Google Scholar 

  85. Zhang, C., Vinyals, O., Munos, R., & Bengio, S. (2018). A study on overfitting in deep reinforcement learning. arXiv preprint

  86. Graves, A. (2016). Adaptive computation time for recurrent neural networks. arXiv preprint

  87. Molchanov, D., Ashukha, A., & Vetrov, D. (2017). Variational dropout sparsifies deep neural networks. In: proceedings of the 34th international conference on machine learning-volume 70: (pp. 2498–2507). JMLR. org

  88. Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., & Garcia-Rodriguez, J. (2017). A review on deep learning techniques applied to semantic segmentation. arXiv preprint

  89. Cui, H., Zhang, H., Ganger, G. R., Gibbons, P. B., & Xing, E. P. (2016, April). Geeps: Scalable deep learning on distributed gpus with a gpu-specialized parameter server. In: proceedings of the eleventh European conference on computer systems (pp. 1–16)

  90. Wang, D., & Shang, Y. (2014). A new active labeling method for deep learning. In: 2014 international joint conference on neural networks (IJCNN) (pp. 112–119). IEEE

  91. Xiao, T., Xia, T., Yang, Y., Huang, C., & Wang, X. (2015). Learning from massive noisy labeled data for image classification. In: proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2691–2699)

  92. Larsson, G., Maire, M., & Shakhnarovich, G. (2016). Learning representations for automatic colorization. In: European conference on computer vision (pp. 577–593). Springer: Cham

  93. Ghose, S., & Prevost, J. J. (2020). AutoFoley: artificial synthesis of synchronized soundtracks for silent videos with deep learning. arXiv preprint

  94. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 60(6):1097–1105

    Google Scholar 

  95. Bai S, An S (2018) A survey on automatic image caption generation. Neurocomputing 311:291–304

    Google Scholar 

  96. Hossain MZ, Sohel F, Shiratuddin MF, Laga H (2019) A comprehensive survey of deep learning for image captioning. ACM Comput Surv (CSUR) 51(6):1–36

    Google Scholar 

  97. Wang, H., Qin, Z., & Wan, T. (2018). Text generation based on generative adversarial nets with latent variables. In: Pacific-Asia conference on knowledge discovery and data mining (pp. 92–103). Springer: Cham

  98. Jaderberg M, Simonyan K, Vedaldi A, Zisserman A (2016) Reading text in the wild with convolutional neural networks. Int J Comput Vision 116(1):1–20

    MathSciNet  Google Scholar 

  99. Guo, J., Lu, S., Cai, H., Zhang, W., Yu, Y., & Wang, J. (2018). Long text generation via adversarial training with leaked information. In: thirty-second AAAI conference on artificial intelligence

  100. Wiseman, S., Shieber, S. M., & Rush, A. M. (2017). Challenges in data-to-document generation. arXiv preprint

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ruchi Mittal.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mittal, R., Arora, S., Bansal, V. et al. An Extensive Study on Deep Learning: Techniques, Applications. Arch Computat Methods Eng 28, 4471–4485 (2021). https://doi.org/10.1007/s11831-021-09542-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11831-021-09542-5

Navigation