The DOI matches its Crossref record; 123 of the 123 references it cites were found in a registry or an index.
A brief survey of machine learning methods and their sensor and IoT applications
Abstract
This paper provides a brief survey of the basic concepts and algorithms used for Machine Learning and its applications. We begin with a broader definition of machine learning and then introduce various learning modalities including supervised and unsupervised methods and deep learning paradigms. In the rest of the paper, we discuss applications of machine learning algorithms in various fields including pattern recognition, sensor networks, anomaly detection, Internet of Things (IoT) and health monitoring. In the final sections, we present some of the software tools and an extensive bibliography.
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Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
(2014) · Cited by 33,419
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Data Mining: Practical Machine Learning Tools and Techniques
(2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,437
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Cited by
Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches
Hasan, M., Islam, M. M., Zarif, M. I. I., & Hashem, M. M. A. (2019) · Internet of Things · doi:10.1016/j.iot.2019.100059 · Cited by 841
Advancing Biosensors with Machine Learning
Cui, F., Yue, Y., Zhang, Y., Zhang, Z., & Zhou, H. S. (2020) · ACS Sensors · doi:10.1021/acssensors.0c01424 · Cited by 768
Machine Learning in IoT Security: Current Solutions and Future Challenges
Hussain, F., Hussain, R., Hassan, S. A., & Hossain, E. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2986444 · Cited by 129
Machine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry Perspective
Bzai, J., Alam, F., Dhafer, A., Bojović, M., Altowaijri, S. M., Niazi, I. K., & Mehmood, R. (2022) · Electronics · doi:10.3390/electronics11172676 · Cited by 110
Integrating machine learning concepts into undergraduate classes
Sahu, C., Ayotte, B., & Banavar, M. K. (2021) · 2021 IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie49875.2021.9637283 · Cited by 3
Metadata
- Authors
- Shanthamallu, U. S., Spanias, A., Tepedelenlioglu, C., & Stanley, M.
- Venue
- 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA)
- Publisher
- IEEE
- Published
- 2017-08
- Pages
- 1-8
- OpenAlex
- W2792005857
- Cited by
- 264
- References
- 123
- FWCI
- 19.48
- Type
- conference paper
- Language
- en
- Open access
- closed
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- The paper
- Shanthamallu, U. S., Spanias, A., Tepedelenlioglu, C., & Stanley, M. (2017). A brief survey of machine learning methods and their sensor and IoT applications. 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA). https://doi.org/10.1109/iisa.2017.8316459
Data sources
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.
