{"id":1027300,"date":"2023-08-04T10:44:41","date_gmt":"2023-08-04T14:44:41","guid":{"rendered":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/uncategorized\/a-comparative-study-of-predicting-the-availability-of-power-line-nature-com-2.php"},"modified":"2023-08-04T10:44:41","modified_gmt":"2023-08-04T14:44:41","slug":"a-comparative-study-of-predicting-the-availability-of-power-line-nature-com-2","status":"publish","type":"post","link":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/machine-learning\/a-comparative-study-of-predicting-the-availability-of-power-line-nature-com-2.php","title":{"rendered":"A comparative study of predicting the availability of power line &#8230; &#8211; Nature.com"},"content":{"rendered":"<p><p>        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).      <\/p>\n<p>        Article ADS PubMed        PubMed        Central         Google Scholar      <\/p>\n<p>        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).      <\/p>\n<p>        Article                Google Scholar      <\/p>\n<p>        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. <a href=\"https:\/\/doi.org\/10.3390\/s22124348\" rel=\"nofollow\">https:\/\/doi.org\/10.3390\/s22124348<\/a>        (2022).      <\/p>\n<p>        Article ADS PubMed        PubMed        Central         Google Scholar      <\/p>\n<p>        Ghasempour, A. Internet of things in smart grid:        Architecture, applications, services, key technologies, and        challenges. Inventions 4(1), 22 (2019).      <\/p>\n<p>        Article                Google Scholar      <\/p>\n<p>        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).      <\/p>\n<p>        Article                Google Scholar      <\/p>\n<p>        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).      <\/p>\n<p>        Article                Google Scholar      <\/p>\n<p>        Brandl, M. & Kellner, K. Performance evaluation of        power-line communication systems for lin-bus based data        transmission. Electronics 10(1), 85 (2021).      <\/p>\n<p>        Article                Google Scholar      <\/p>\n<p>        Prasad, G. & Lampe, L. Full-duplex power line        communications: Design and applications from multimedia to        smart grid. IEEE Commun. Magaz. 58(2),        106112 (2019).      <\/p>\n<p>        Article         Google Scholar      <\/p>\n<p>        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).      <\/p>\n<p>        Article                Google Scholar      <\/p>\n<p>        Wang, B. & Cao, Z. A review of impedance matching        techniques in power line communications. Electronics        8(9), 1022 (2019).      <\/p>\n<p>        Article                Google Scholar      <\/p>\n<p>        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).      <\/p>\n<p>        Article                Google Scholar      <\/p>\n<p>        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).      <\/p>\n<p>                Google Scholar      <\/p>\n<p>        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      <\/p>\n<p>        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).      <\/p>\n<p>        Chapter                Google Scholar      <\/p>\n<p>        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      <\/p>\n<p>        Stiri, S. et al. Hybrid plc and lorawan smart        metering networks: Modeling and optimization. IEEE        Trans. Indus. Inf. 18(3), 15721582 (2021).      <\/p>\n<p>        Article         Google Scholar      <\/p>\n<p>        Ullah, Z., Al-Turjman, F., Mostarda, L. & Gagliardi, R.        Applications of artificial intelligence and machine        learning in smart cities. Comput. Commun.        154, 313323 (2020).      <\/p>\n<p>        Article                Google Scholar      <\/p>\n<p>        Mata, J. et al. Artificial intelligence (ai) methods        in optical networks: A comprehensive survey. Optic.        Swit. Netw. 28, 4357 (2018).      <\/p>\n<p>        Article                Google Scholar      <\/p>\n<p>        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).      <\/p>\n<p>        Article                Google Scholar      <\/p>\n<p>        Yang, H. et al. Artificial-intelligence-enabled        intelligent 6G networks. IEEE Netw. 34(6),        272280 (2020).      <\/p>\n<p>        Article         Google Scholar      <\/p>\n<p>        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).      <\/p>\n<p>        Article                Google Scholar      <\/p>\n<p>        Zhang, C. & Lu, Y. Study on artificial intelligence: The        state of the art and future prospects. J. Indus. Inf.        Integr. 23, 100224. <a href=\"https:\/\/doi.org\/10.1016\/j.jii.2021.100224\" rel=\"nofollow\">https:\/\/doi.org\/10.1016\/j.jii.2021.100224<\/a>        (2021).      <\/p>\n<p>        Article                Google Scholar      <\/p>\n<p>        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).      <\/p>\n<p>        Article         Google Scholar      <\/p>\n<p>        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) <a href=\"https:\/\/doi.org\/10.1109\/isplc.2018.8360239\" rel=\"nofollow\">https:\/\/doi.org\/10.1109\/isplc.2018.8360239<\/a>      <\/p>\n<p>        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) <a href=\"https:\/\/doi.org\/10.1109\/isplc.2019.8693272\" rel=\"nofollow\">https:\/\/doi.org\/10.1109\/isplc.2019.8693272<\/a>      <\/p>\n<p>        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. <a href=\"https:\/\/doi.org\/10.1016\/j.eswa.2021.115884\" rel=\"nofollow\">https:\/\/doi.org\/10.1016\/j.eswa.2021.115884<\/a>        (2022).      <\/p>\n<p>        Article                Google Scholar      <\/p>\n<p>        Bade, K., & Nurnberger, A. Personalized hierarchical        clustering. In: 2006 IEEE\/WIC\/ACM International Conference        on Web Intelligence (WI 2006 Main Conference        Proceedings)(WI06) (2006) <a href=\"https:\/\/doi.org\/10.1109\/wi.2006.131\" rel=\"nofollow\">https:\/\/doi.org\/10.1109\/wi.2006.131<\/a>      <\/p>\n<p>        Leskovec, J., Rajaraman, A. & Ullman, J. D. Mining of        Massive Datasets (Cambridge University Press, 2014).      <\/p>\n<p>        Book         Google Scholar      <\/p>\n<p>        Vesanto, J. & Alhoniemi, E. Clustering of the        self-organizing map. IEEE Trans. Neural Netw.        11(3), 586600. <a href=\"https:\/\/doi.org\/10.1109\/72.846731\" rel=\"nofollow\">https:\/\/doi.org\/10.1109\/72.846731<\/a>        (2000).      <\/p>\n<p>        Article CAS PubMed                Google Scholar      <\/p>\n<p>        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. <a href=\"https:\/\/doi.org\/10.1049\/iet-com.2014.0464\" rel=\"nofollow\">https:\/\/doi.org\/10.1049\/iet-com.2014.0464<\/a>        (2015).      <\/p>\n<p>        Article                Google Scholar      <\/p>\n<p>        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). <a href=\"https:\/\/doi.org\/10.1109\/ICEEM52022.2021.9480638\" rel=\"nofollow\">https:\/\/doi.org\/10.1109\/ICEEM52022.2021.9480638<\/a>      <\/p>\n<p>        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)        <a href=\"https:\/\/doi.org\/10.1109\/icetet.2010.134\" rel=\"nofollow\">https:\/\/doi.org\/10.1109\/icetet.2010.134<\/a>      <\/p>\n<p>        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).      <\/p>\n<p>        Article                Google Scholar      <\/p>\n<p>        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) <a href=\"https:\/\/doi.org\/10.1109\/wiecon-ece48653.2019.9019970\" rel=\"nofollow\">https:\/\/doi.org\/10.1109\/wiecon-ece48653.2019.9019970<\/a>      <\/p>\n<p>        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).      <\/p>\n<p>        Article         Google Scholar      <\/p>\n<p>        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. <a href=\"https:\/\/doi.org\/10.1109\/jiot.2020.2994200\" rel=\"nofollow\">https:\/\/doi.org\/10.1109\/jiot.2020.2994200<\/a>        (2020).      <\/p>\n<p>        Article                Google Scholar      <\/p>\n<p>        Bhushan, S. et al. An experimental analysis of        various machine learning algorithms for hand gesture        recognition. Electronics 11(6), 968. <a href=\"https:\/\/doi.org\/10.3390\/electronics11060968\" rel=\"nofollow\">https:\/\/doi.org\/10.3390\/electronics11060968<\/a>        (2022).      <\/p>\n<p>        Article                Google Scholar      <\/p>\n<p>        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.<a href=\"https:\/\/doi.org\/10.1016\/b978-0-12-816718-2.00021-x\" rel=\"nofollow\">https:\/\/doi.org\/10.1016\/b978-0-12-816718-2.00021-x<\/a>        (2019).      <\/p>\n<p>        Article                Google Scholar      <\/p>\n<p>        Heydarian, M., Doyle, T. E. & Samavi, R. Mlcm: Multi-label        confusion matrix. IEEE Access 10,        1908319095. <a href=\"https:\/\/doi.org\/10.1109\/access.2022.3151048\" rel=\"nofollow\">https:\/\/doi.org\/10.1109\/access.2022.3151048<\/a>        (2022).      <\/p>\n<p>        Article                Google Scholar      <\/p>\n<p>        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. <a href=\"https:\/\/doi.org\/10.3390\/electronics8030322\" rel=\"nofollow\">https:\/\/doi.org\/10.3390\/electronics8030322<\/a>        (2019).      <\/p>\n<p>        Article                Google Scholar      <\/p>\n<p><!-- Auto Generated --><\/p>\n<p>Read more here: <\/p>\n<p><a target=\"_blank\" rel=\"nofollow noopener\" href=\"https:\/\/www.nature.com\/articles\/s41598-023-39120-7\" title=\"A comparative study of predicting the availability of power line ... - Nature.com\">A comparative study of predicting the availability of power line ... - Nature.com<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p> Mlnek, P., Rusz, M., Benel, L., Slik, J.  <a href=\"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/machine-learning\/a-comparative-study-of-predicting-the-availability-of-power-line-nature-com-2.php\">Continue reading <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"limit_modified_date":"","last_modified_date":"","_lmt_disableupdate":"","_lmt_disable":"","footnotes":""},"categories":[1231415],"tags":[],"class_list":["post-1027300","post","type-post","status-publish","format-standard","hentry","category-machine-learning"],"modified_by":null,"_links":{"self":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/posts\/1027300"}],"collection":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/comments?post=1027300"}],"version-history":[{"count":0,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/posts\/1027300\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/media?parent=1027300"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/categories?post=1027300"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/tags?post=1027300"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}