Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12188/30412
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cvitić, Ivan | en_US |
dc.contributor.author | Jevremovic, Aleksandar | en_US |
dc.contributor.author | Lameski, Petre | en_US |
dc.date.accessioned | 2024-06-05T12:06:12Z | - |
dc.date.available | 2024-06-05T12:06:12Z | - |
dc.date.issued | 2023-10-13 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.12188/30412 | - |
dc.description.abstract | Artificial intelligence can be considered a leading component of industrial transformation, but its application is also present in other areas, such as the telecommunications sector. Methodologies based on artificial intelligence, the most significant of which is machine learning, support these areas when predicting maintenance needs and reducing downtime. Machine learning has many algorithms and methods of its own. This research presents machine learning methods and the possibilities of their use in the areas of telecommunications and Industry 4.0. With the improvement of new technologies such as higher internet speeds and 5G mobile networks in the field of telecommunications or Industry 4.0, comes the need for new and improved management and support for systems that use these new technologies. The possibilities of current Artificial Intelligence of Things devices and distributed execution of ML algorithms using Edge/Fog/Cloud computing environments are being considered. Some types of machine learning can be used for collecting data to improve user’s quality of service. Also, some types of them can be used to collect data on network traffic or, in general, for any system that needs to collect data, cluster data points, and analyze data. | en_US |
dc.publisher | Springer US | en_US |
dc.relation.ispartof | Mobile Networks and Applications | en_US |
dc.title | Approaches and Opportunities of Using Machine Learning Methods in Telecommunications and Industry 4.0 | en_US |
dc.type | Journal Article | en_US |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
crisitem.author.dept | Faculty of Computer Science and Engineering | - |
Appears in Collections: | Faculty of Computer Science and Engineering: Journal Articles |
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