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. 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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
European Commission. (2020). White paper on artificial intelligence – A European approach to excellence and trust. Brussels.
Wilson, A. A brief introduction to supervised learning. https://towardsdatascience.com/a-brief-introduction-to-supervised-learning-54a3e3932590. Accessed 4 June 2022.
Mishra, S. Unsupervised learning and data clustering. https://towardsdatascience.com/unsupervised-learning-and-data-clustering-eeecb78b422a. Accessed 4 June 2022.
Lawtomated. Supervised learning vs unsupervised learning. Which is better? https://lawtomated.com/supervised-vs-unsupervised-learning-which-is-better/. Accessed 4 June 2022.
Altexsoft. Unsupervised learning: Algorithms and examples. https://www.altexsoft.com/blog/unsupervised-machine-learning/. Accessed 4 June 2022.
Batta, M. (2020). Machine learning algorithms – A review. International Journal of Science and Research, 9, 381–386. https://doi.org/10.21275/ART20203995
Johnson, D. Reinforcement learning tutorial. https://www.guru99.com/reinforcement-learning-tutorial.html. Accessed 4 June 2022.
Brownlee, J. Machine learning tools. https://machinelearningmastery.com/machine-learning-tools/. Accessed 4 June 2022.
My server name. 11 most popular machine learning software tools 2021. https://hr.myservername.com/11-most-popular-machine-learning-software-tools-2021. Accessed 4 June 2022.
Scikit-learn. Machine learning in Python. https://scikit-learn.org/stable/. Accessed 4 June 2022.
TensorFlow. TensorFlow. https://www.tensorflow.org/. Accessed 4 June 2022.
Kovalev, S., Sintsov, S., Khizhniak, A., & Turol, S. Implementing k-means clustering with TensorFlow. https://www.altoros.com/blog/using-k-means-clustering-in-tensorflow/. Accessed 4 June 2022.
PyTorch. PyTorch. https://pytorch.org/. Accessed 4 June 2022.
TutorialsPoint. What is Weka? https://www.tutorialspoint.com/weka/what_is_weka.htm. Accessed 1 July 2022.
Weka 3. Weka 3: Machine learning software in Java. https://www.cs.waikato.ac.nz/ml/weka/. Accessed 1 July 2022.
Brownlee, J. How to run your first classifier in Weka. https://machinelearningmastery.com/how-to-run-your-first-classifier-in-weka/. Accessed 1 July 2022.
RapidMiner. https://rapidminer.com/platform/. Accessed 1 July 2022.
Burke, J. What is the role of machine learning in networking? https://searchnetworking.techtarget.com/answer/What-is-the-role-of-machine-learning-in-networking. Accessed 4 July 2022.
Stouffer, C. DDoS attacks: A simplified guide + DDoS attack protection tips. https://us.norton.com/internetsecurity-emerging-threats-ddos-attacks.html. Accessed 4 July 2022.
Perakovic, D., Perisa, M., Cvitic, I., & Husnjak, S. (2017). Model for detection and classification of DDoS traffic based on artificial neural network. Telfor Journal, 9, 26–31. https://doi.org/10.5937/telfor1701026P
Clark, J. What is the Internet of Things (IoT)? https://www.ibm.com/blogs/internet-of-things/what-is-the-iot/. Accessed 4 July 2022.
Cvitic, I., Perakovic, D., Gupta, B. B., & Choo, K.-K. R. (2022). Boosting-based DDoS detection in internet of things systems. IEEE Internet of Things Journal, 9, 2109–2123. https://doi.org/10.1109/JIOT.2021.3090909
Phishing.org. What is phishing? https://www.phishing.org/what-is-phishing. Accessed 4 July 2022.
Gupta, B. B., Tewari, A., Cvitić, I., Peraković, D., & Chang, X. (2022). Artificial intelligence empowered emails classifier for Internet of Things based systems in industry 4.0. Wireless Networks, 28, 493–503. https://doi.org/10.1007/s11276-021-02619-w
Norton. Phishing email examples to help you identify phishing scams. https://us.norton.com/internetsecurity-online-scams-phishing-email-examples.html. Accessed 1 July 2022.
Cvitić, I., Peraković, D., Periša, M., & Gupta, B. (2021). Ensemble machine learning approach for classification of IoT devices in smart home. International Journal of Machine Learning and Cybernetics, 12, 3179–3202. https://doi.org/10.1007/s13042-020-01241-0
Jamshidi, S. (2019). The applications of machine learning techniques in networking.
Akhtar, M., & Moridpour, S. (2021). A review of traffic congestion prediction using. Journal of Advanced Transportation, 2021, 18. https://doi.org/10.1155/2021/8878011
Joshi, M., & Hadi, T. (2015). A review of network traffic analysis and prediction techniques. ArXiv. abs/1507.0. https://doi.org/10.48550/arXiv.1507.05722
Zorić, P., Musa, M., & Kuljanić, T. M. (2021). Smart factory environment: Review of security threats and risks. In Lecture notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (pp. 203–214). https://doi.org/10.1007/978-3-030-78459-1_15
Peraković, D., Periša, M., Cvitić, I., & Zorić, P. (2021). Artificial intelligence application in different scenarios of the networked society 5.0 environment. In Zbornik Radova Trideset Devetog Simpozijuma O Novim Tehnologijama U Poštanskom I Telekomunikacionom Saobraćaju – Postel 2021. University of Belgrade, Faculty of Transport and Traffic Engineering. https://doi.org/10.37528/FTTE/9788673954455/POSTEL.2021.021
Gonfalonieri, A. How to implement machine learning for predictive maintenance. https://towardsdatascience.com/how-to-implement-machine-learning-for-predictive-maintenance-4633cdbe4860. Accessed 4 July 2022.
Wang, M., Cui, Y., Wang, X., Xiao, S., & Jiang, J. (2017). Machine learning for networking: Workflow, advances and opportunities. IEEE Network, 32, 92–99. https://doi.org/10.1109/MNET.2017.1700200
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Peraković, D., Periša, M., Cvitić, I., Zorić, P., Kuljanić, T.M., Aleksić, D. (2023). Opportunities of Using Machine Learning Methods in Telecommunications and Industry 4.0 – A Survey. In: Knapčíková, L., Peraković, D. (eds) 7th EAI International Conference on Management of Manufacturing Systems. MMS 2022. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-22719-6_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-22719-6_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-22718-9
Online ISBN: 978-3-031-22719-6
eBook Packages: EngineeringEngineering (R0)