The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Artificial intelligence, machine learning and deep learning
Abstract
It is increasingly recognized that artificial intelligence has been touted as a new mobile. Because of the high volume of data that being generated by devices, sensors and social media users, the machine can learn to distinguish the pattern and makes a reasonably good prediction. This article will explore the use of machine learning and its methodologies. Furthermore, the field of deep learning which is being exploited in many leading IT providers will be clarified and discussed.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
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LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058
Deep Learning: Methods and Applications
Deng, L. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988157 · Cited by 1,759
(2011) · doi:10.5591/978-1-57735-516-8/ijcai11-210 · Cited by 1,229
Learning multiple layers of representation
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Building high-level features using large scale unsupervised learning
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Machine Learning in Medical Imaging
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Cited by
AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications
Rashid, A. B., & Kausik, M. A. K. (2024) · Hybrid Advances · doi:10.1016/j.hybadv.2024.100277 · Cited by 764
Bekbolatova, M., Mayer, J., Ong, C. W., & Toma, M. (2024) · Healthcare · doi:10.3390/healthcare12020125 · Cited by 404
Nazar, M., Alam, M. M., Yafi, E., & Su'ud, M. M. (2021) · IEEE Access · doi:10.1109/access.2021.3127881 · Cited by 312
A Survey on Task Offloading in Multi-access Edge Computing
Islam, A., Debnath, A., Ghose, M., & Chakraborty, S. (2021) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2021.102225 · Cited by 271
Demystifying AI: Current State and Future Role in Medical Education Assessment
Turner, L., Hashimoto, D. A., Vasisht, S., & Schaye, V. (2024) · Academic Medicine · doi:10.1097/acm.0000000000005598 · Cited by 70
Metadata
- Authors
- Ongsulee, P.
- Venue
- 2017 15th International Conference on ICT and Knowledge Engineering (ICT&KE)
- Publisher
- IEEE
- Published
- 2017-11
- Pages
- 1-6
- OpenAlex
- W2783616455
- Cited by
- 367
- References
- 18
- FWCI
- 4.83
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Ongsulee, P. (2017). Artificial intelligence, machine learning and deep learning. 2017 15th International Conference on ICT and Knowledge Engineering (ICT&KE). https://doi.org/10.1109/ictke.2017.8259629
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
