A Survey on Deep Learning Techniques in Real-Time Applications

Kanthi Rekha Miriyala*, Dhana Lakshmi Gorle**, Suneetha Eluri***
*-*** Department of Computer Science and Engineering, University College of Engineering Kakinada, Jawaharlal Nehru Technological University Kakinada, Andhra Pradesh, India.
Periodicity:January - June'2022
DOI : https://doi.org/10.26634/jpr.9.1.18858


In recent years, machine learning and Deep Learning have increased and gathered epic success in traditional application domains and new areas of Artificial Intelligence. The performance using Deep Learning has dominated experimental results compared to conventional machine learning algorithms. This paper presents an overview of the progress that has occurred in Deep Learning (DL) concerning some application domains like Autonomous Driving, Healthcare, Voice Recognition, Image Recognition, Advertising, Predicting Natural Calamities, National Stock Exchange and many more. Additionally, deeper insights into several Deep Learning techniques, their working principles, and experimental results are scrutinized. The survey covers Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL).


Deep Learning, Machine Learning, Supervised, Unsupervised, Reinforcement Learning.

How to Cite this Article?

Miriyala, K. R., Gorle, D. L., and Eluri, S. (2022). A Survey on Deep Learning Techniques in Real-Time Applications. i-manager’s Journal on Pattern Recognition, 9(1), 33-40. https://doi.org/10.26634/jpr.9.1.18858


[1]. Abdel-Hamid, O., Deng, L., Yu, D., & Jiang, H. (2013). Deep segmental neural networks for speech recognition. In Interspeech, 36(70). 1849-1853.
[2]. Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., ... & Asari, V. K. (2019). A state-ofthe- art survey on deep learning theory and architectures. Electronics, 8(3), 292. https://doi.org/10.3390/electronics8030292
[3]. Bengio, Y. (2009). Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2(1), 1-127. http://dx.doi.org/10.1561/2200000006
[4]. Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828. https://doi.org/10.1109/TPAMI.2013.50
[5]. Canziani, A., Paszke, A., & Culurciello, E. (2016). An analysis of deep neural network models for practical applications. arXiv preprint arXiv:1605.07678. https://doi.org/10.48550/arXiv.1605.07678
[6]. Deng, L., Abdel-Hamid, O., & Yu, D. (2013). A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 6669-6673. https://doi.org/10.1109/ICASSP.2013.6638952
[7]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770-778.
[8]. He, Y., & Fosler-Lussier, E. (2012). Efficient segmental conditional random fields for phone recognition. In Proceedings of the Annual Conference of the International Speech Communication Association, 1898-1901.
[9]. Karpathy, A., & Fei-Fei, L. (2015). Deep visualsemantic alignments for generating image descriptions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3128-3137
[10]. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems, 1, 1097-1105.
[11]. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436-444. https://doi.org/10.1038/nature14539
[12]. Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013). Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602. https://doi.org/10.48550/arXiv.1312.5602
[13]. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533. https://doi.org/10.1038/nature14236
[14]. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85- 117.https://doi.org/10.1016/j.neunet.2014.09.003
[15]. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXivpreprint arXiv:1409.1556. https://doi.org/10.48550/arXiv.1409.1556
[16]. Song, W., & Cai, J. (2015). End-to-end deep neural network for automatic speech recognition. Standford CS224D Reports, 1-8.
[17]. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,1-9.
[18]. Tang, H., Wang, W., Gimpel, K., & Livescu, K. (2015). Discriminative segmental cascades for feature-rich phone recognition. In 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), 561-568. https://doi.org/10.1109/ASRU.2015.7404845
[19]. Zeiler, M. D.,& Fergus, R. (2013). Visualizing and understanding convolutional networks. arXiv2013, arXiv:1311.2901. https://doi.org/10.48550/arXiv.1311.2901
[20]. Zhang, L., Yang, F., Zhang, Y. D., & Zhu, Y. J. (2016). Road crack detection using deep convolutional neural network. In 2016 IEEE international conference on image processing (ICIP), 3708-3712. https://doi.org/10.1109/ICIP.2016.7533052
[21]. Zingade, A. (2018). Autonomous Driving using Deep Learning and Behavioural Cloning. Retrieved from https://medium.com/@akarshzingade/autonomousdriving- using-deep-learning-and-behavioural-cloning-97983a57fe10
[22]. Zweig, G. (2012). Classification and recognition with direct segment models. In 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4161-4164. https://doi.org/10.1109/ICASSP.2012.6288835
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.