A Comprehensive Survey of Deep Learning Models Across Diverse Application Domains
Keywords:
Deep learning, artificial intelligence, domain applications, CNN, RNN, GAN, Transformer, GNN.Abstract
Deep learning, a significant domain of modern artificial intelligence, provides reliable solutions to an extensive scope of convoluted problems. This paper specifies a comprehensive outline of the most well-known deep learning models and the roles in a range of domains, including computer vision, cybersecurity, natural language processing, autonomous systems and healthcare. We look at the architecture, benefits, drawbacks, and performance of several models, comprising convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), transformers, and graph neural networks (GNNs). Comprehending a systematic literature review, we present perceptions into what way these models have transformed their relevant areas, converse evolving tendencies, and ascertain probable areas for further research.
Downloads
References
Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv:1409.0473.
Bojarski, M., et al. (2016). End to end learning for self-driving cars. arXiv:1604.07316.
Brown, T., et al. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33.
Chen, L. C., et al. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. ECCV.
Devlin, J., et al. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. NAACL.
Gawehn, E., Hiss, J. A., & Schneider, G. (2016). Deep learning in drug discovery. Molecular Informatics, 35(1), 3-14.
Goodfellow, I., et al. (2014). Generative adversarial nets. NeurIPS.
Graves, A., Mohamed, A. R., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. ICASSP.
He, K., et al. (2016). Deep residual learning for image recognition. CVPR.
Huang, W., & Stokes, J. W. (2016). MtNet: A multi-task neural network for dynamic malware classification. Detection of Intrusions and Malware.
Kim, G., Lee, S., & Kim, S. (2016). A novel hybrid intrusion detection method integrating anomaly detection with misuse detection. Expert Systems with Applications, 41(4), 1690-1700.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. NeurIPS.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
Litjens, G., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.
Liu, Y., & Lapata, M. (2019). Text summarization with pretrained encoders. EMNLP.
Miotto, R., et al. (2016). Deep patient: An unsupervised representation to predict the future of patients from the electronic health records. Scientific Reports, 6(1), 1-10.
Mikolov, T., et al. (2010). Recurrent neural network based language model. Interspeech.
Pathak, D., et al. (2016). Context encoders: Feature learning by inpainting. CVPR.
Redmon, J., et al. (2016). You only look once: Unified, real-time object detection. CVPR.
Ren, S., et al. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. NeurIPS.
Ronneberger, O., et al. (2015). U-Net: Convolutional networks for biomedical image segmentation. MICCAI.
Socher, R., et al. (2013). Recursive deep models for semantic compositionality over a sentiment treebank. EMNLP.
Vaswani, A., et al. (2017). Attention is all you need. NeurIPS.
Wu, Z., et al. (2020). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems.
Zitnik, M., et al. (2018). Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, 34(13), i457-i466.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
