Mlnek, P., Rusz, M., Benel, L., Slik, J. & Musil, P. Possibilities of broadband power line communications for smart home and smart building applications. Sensors 21(1), 240 (2021).
Article ADS PubMed PubMed Central Google Scholar
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M. & Ayyash, M. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 17(4), 23472376 (2015).
Article Google Scholar
Gonzlez-Ramos, J. et al. Upgrading the power grid functionalities with broadband power line communications: Basis, applications, current trends and challenges. Sensors 22(12), 4348. https://doi.org/10.3390/s22124348 (2022).
Article ADS PubMed PubMed Central Google Scholar
Ghasempour, A. Internet of things in smart grid: Architecture, applications, services, key technologies, and challenges. Inventions 4(1), 22 (2019).
Article Google Scholar
Hamamreh, J. M., Furqan, H. M. & Arslan, H. Classifications and applications of physical layer security techniques for confidentiality: A comprehensive survey. IEEE Commun. Surv. Tutor. 21(2), 17731828 (2018).
Article Google Scholar
Vincent, T. A., Gulsoy, B., Sansom, J. E. & Marco, J. Development of an in-vehicle power line communication network with in-situ instrumented smart cells. Transp. Eng. 6, 100098 (2021).
Article Google Scholar
Brandl, M. & Kellner, K. Performance evaluation of power-line communication systems for lin-bus based data transmission. Electronics 10(1), 85 (2021).
Article Google Scholar
Prasad, G. & Lampe, L. Full-duplex power line communications: Design and applications from multimedia to smart grid. IEEE Commun. Magaz. 58(2), 106112 (2019).
Article Google Scholar
Rocha Farias, L., Monteiro, L. F., Leme, M. O. & Stevan, S. L. Jr. Empirical analysis of the communication in industrial environment based on g3-power line communication and influences from electrical grid. Electronics 7(9), 194 (2018).
Article Google Scholar
Wang, B. & Cao, Z. A review of impedance matching techniques in power line communications. Electronics 8(9), 1022 (2019).
Article Google Scholar
Oliveira, R. M., Vieira, A. B., Latchman, H. A. & Ribeiro, M. V. Medium access control protocols for power line communication: A survey. IEEE Commun. Surv. Tutor. 21(1), 920939 (2018).
Article Google Scholar
Appasani, B. & Mohanta, D. K. A review on synchrophasor communication system: Communication technologies, standards and applications. Protect. Control Mod. Power Syst. 3(1), 117 (2018).
Google Scholar
Sanz, A., Sancho, D., & Ibar, J.C. Performances of g3 plc-rf hybrid communication systems. In: 2021 IEEE International Symposium on Power Line Communications and Its Applications (ISPLC), pp. 6772 (2021). IEEE
Deru, L., Dawans, S., Ocaa, M., Quoitin, B. & Bonaventure, O. Redundant border routers for mission-critical 6lowpan networks. In Real-world Wireless Sensor Networks (ed. Dev, T.) 195203 (Springer, 2014).
Chapter Google Scholar
Kassab, A.S., Seddik, K.G., Elezabi, A., & Soltan, A. Realistic wireless smart-meter network optimization using composite rpl metric. In: 2020 8th International Conference on Smart Grid (icSmartGrid), pp. 109114 (2020). IEEE
Stiri, S. et al. Hybrid plc and lorawan smart metering networks: Modeling and optimization. IEEE Trans. Indus. Inf. 18(3), 15721582 (2021).
Article Google Scholar
Ullah, Z., Al-Turjman, F., Mostarda, L. & Gagliardi, R. Applications of artificial intelligence and machine learning in smart cities. Comput. Commun. 154, 313323 (2020).
Article Google Scholar
Mata, J. et al. Artificial intelligence (ai) methods in optical networks: A comprehensive survey. Optic. Swit. Netw. 28, 4357 (2018).
Article Google Scholar
Fu, Y., Wang, S., Wang, C.-X., Hong, X. & McLaughlin, S. Artificial intelligence to manage network traffic of 5G wireless networks. IEEE Netw. 32(6), 5864 (2018).
Article Google Scholar
Yang, H. et al. Artificial-intelligence-enabled intelligent 6G networks. IEEE Netw. 34(6), 272280 (2020).
Article Google Scholar
Shi, Y., Yang, K., Jiang, T., Zhang, J. & Letaief, K. B. Communication-efficient edge AI: Algorithms and systems. IEEE Commun. Surv. Tutor. 22(4), 21672191 (2020).
Article Google Scholar
Zhang, C. & Lu, Y. Study on artificial intelligence: The state of the art and future prospects. J. Indus. Inf. Integr. 23, 100224. https://doi.org/10.1016/j.jii.2021.100224 (2021).
Article Google Scholar
Balada, C. et al. Fhler-im-netz: A smart grid and power line communication data set. IET Smart Gridhttps://doi.org/10.1049/stg2.12093 (2022).
Article Google Scholar
Righini, D., Tonello, A.M. Noise determinism in multi-conductor narrow band plc channels. In: 2018 IEEE International Symposium on Power Line Communications and its Applications (ISPLC) (2018) https://doi.org/10.1109/isplc.2018.8360239
Righini, D., Tonello, A.M.: Automatic clustering of noise in multi-conductor narrow band plc channels. In: 2019 IEEE International Symposium on Power Line Communications and its Applications (ISPLC) (2019) https://doi.org/10.1109/isplc.2019.8693272
Reyes, D. M. A., Souza, R. M. C. R. & Oliveira, A. L. I. A three-stage approach for modeling multiple time series applied to symbolic quartile data. Exp. Syst. Appl. 187, 115884. https://doi.org/10.1016/j.eswa.2021.115884 (2022).
Article Google Scholar
Bade, K., & Nurnberger, A. Personalized hierarchical clustering. In: 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI06) (2006) https://doi.org/10.1109/wi.2006.131
Leskovec, J., Rajaraman, A. & Ullman, J. D. Mining of Massive Datasets (Cambridge University Press, 2014).
Book Google Scholar
Vesanto, J. & Alhoniemi, E. Clustering of the self-organizing map. IEEE Trans. Neural Netw. 11(3), 586600. https://doi.org/10.1109/72.846731 (2000).
Article CAS PubMed Google Scholar
Dubey, A., Mallik, R. K. & Schober, R. Performance analysis of a multi-hop power line communication system over log-normal fading in presence of impulsive noise. IET Commun. 9(1), 19. https://doi.org/10.1049/iet-com.2014.0464 (2015).
Article Google Scholar
Hossam, M., Afify, A.A., Rady, M., Nabil, M., Moussa, K., Yousri, R., & Darweesh, M.S. A comparative study of different face shape classification techniques. In: 2021 International Conference on Electronic Engineering (ICEEM), pp. 16 (2021). https://doi.org/10.1109/ICEEM52022.2021.9480638
Prajapati, G.L., & Patle, A. On performing classification using svm with radial basis and polynomial kernel functions. In: 2010 3rd International Conference on Emerging Trends in Engineering and Technology (2010) https://doi.org/10.1109/icetet.2010.134
Almaiah, M. A. et al. Performance investigation of principal component analysis for intrusion detection system using different support vector machine kernels. Electronics 11(21), 3571 (2022).
Article Google Scholar
Verma, A.R., Singh, S.P., Mishra, R.C., & Katta, K. Performance analysis of speaker identification using gaussian mixture model and support vector machine. In: 2019 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE) (2019) https://doi.org/10.1109/wiecon-ece48653.2019.9019970
Khan, M. Y. et al. Automated prediction of good dictionary examples (gdex): A comprehensive experiment with distant supervision, machine learning, and word embedding-based deep learning techniques. Complexityhttps://doi.org/10.1155/2021/2553199 (2021).
Article Google Scholar
Liu, P., Zhang, Y., Wu, H. & Fu, T. Optimization of edge-plc-based fault diagnosis with random forest in industrial internet of things. IEEE Internet Things J. 7(10), 96649674. https://doi.org/10.1109/jiot.2020.2994200 (2020).
Article Google Scholar
Bhushan, S. et al. An experimental analysis of various machine learning algorithms for hand gesture recognition. Electronics 11(6), 968. https://doi.org/10.3390/electronics11060968 (2022).
Article Google Scholar
Abirami, S. P., Kousalya, G. & Karthick, R. Varied expression analysis of children with ASD using multimodal deep learning technique. Deep Learn. Parallel Comput. Environ. Bioeng. Syst.https://doi.org/10.1016/b978-0-12-816718-2.00021-x (2019).
Article Google Scholar
Heydarian, M., Doyle, T. E. & Samavi, R. Mlcm: Multi-label confusion matrix. IEEE Access 10, 1908319095. https://doi.org/10.1109/access.2022.3151048 (2022).
Article Google Scholar
Abdulhammed, R., Musafer, H., Alessa, A., Faezipour, M. & Abuzneid, A. Features dimensionality reduction approaches for machine learning based network intrusion detection. Electronics 8(3), 322. https://doi.org/10.3390/electronics8030322 (2019).
Article Google Scholar
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