The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
A Review of Machine Learning and Deep Learning Applications
Abstract
Machine learning is one of the fields in the modern computing world. A plenty of research has been undertaken to make machines intelligent. Learning is a natural human behavior which has been made an essential aspect of the machines as well. There are various techniques devised for the same. Traditional machine learning algorithms have been applied in many application areas. Researchers have put many efforts to improve the accuracy of that machinelearning algorithms. Another dimension was given thought which leads to deep learning concept. Deep learning is a subset of machine learning. So far few applications of deep learning have been explored. This is definitely going to cater to solving issues in several new application domains, sub-domains using deep learning. A review of these past and future application domains, sub-domains, and applications of machine learning and deep learning are illustrated in this paper.
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Cites
Deep Learning for Health Informatics
Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G.-Z. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2636665 · Cited by 2,000
Deep learning for sentiment analysis: A survey
Zhang, L., Wang, S., & Liu, B. (2018) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1253 · Cited by 1,928
Deep Direct Reinforcement Learning for Financial Signal Representation and Trading
Deng, Y., Bao, F., Kong, Y., Ren, Z., & Dai, Q. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2522401 · Cited by 822 · cited 1×
“A prediction domain topic such as Financial Signal Representation and Trading can be very well be implemented usingDirect Deep Reinforcement Learning technique in this paper [19].”
MethodA review of supervised machine learning algorithms
Singh, A., Thakur, N., & Sharma, A. (2016) · International Conference on Computing for Sustainable Global Development · Cited by 583
Deep learning in microscopy image analysis: a survey
Xing, F., Xie, Y., Su, H., Liu, F., & Yang, L. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2766168 · Cited by 460
Machine learning and its applications: A review
Angra, S., & Ahuja, S. (2017) · 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC) · doi:10.1109/icbdaci.2017.8070809 · Cited by 242
Du, X., Cai, Y., Wang, S., & Zhang, L. (2016) · 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac.2016.7804882 · Cited by 220
Comparative study of machine learning algorithms for breast cancer detection and diagnosis
Bazazeh, D., & Shubair, R. (2016) · 2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA) · doi:10.1109/icedsa.2016.7818560 · Cited by 188
A Survey on Deep Learning in Big Data
Gheisari, M., Wang, G., & Bhuiyan, M. Z. A. (2017) · IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) · doi:10.1109/cse-euc.2017.215 · Cited by 161
A comparative study on machine learning algorithms for indoor positioning
Keser, S. B., Elibol, G., Günal, S., & Yayan, U. (2015) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista.2015.7276725 · Cited by 145
Cited by
Digital twin: Enabling technologies, challenges and open research
Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020) · IEEE Access · doi:10.1109/access.2020.2998358 · Cited by 2,965
Wang, B., Li, Y., Zhou, M., Han, Y., Zhang, M., Gao, Z., Liu, Z., Chen, P., Du, W., Zhang, X., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36017-x · Cited by 259
Ahmad, S., Shakeel, I., Mehfuz, S., & Ahmad, J. (2023) · Computer Science Review · doi:10.1016/j.cosrev.2023.100568 · Cited by 197
Key Challenges and Emerging Technologies in Industrial IoT Architectures: A Review
Mirani, A. A., Velasco-Hernandez, G., Awasthi, A., & Walsh, J. (2022) · Sensors · doi:10.3390/s22155836 · Cited by 119
Alqarni (2024) · doi:10.1016/j.caeai.2024.100205 · Cited by 62
Metadata
- Authors
- Shinde, P. P., & Shah, S.
- Venue
- 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)
- Publisher
- IEEE
- Published
- 2018-08
- Pages
- 1-6
- OpenAlex
- W2941584439
- Cited by
- 773
- References
- 14
- FWCI
- 4.79
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Shinde, P. P., & Shah, S. (2018). A Review of Machine Learning and Deep Learning Applications. 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). https://doi.org/10.1109/iccubea.2018.8697857
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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.
