Computer Science for Green Technologies and Sustainable Development: Invited session Report at KES 2023
| dc.contributor.author | Zema, Tomasz | |
| dc.date.accessioned | 2023-12-19T12:41:53Z | |
| dc.date.available | 2023-12-19T12:41:53Z | |
| dc.date.issued | 2023-12-11 | |
| dc.identifier.uri | http://hdl.handle.net/11089/48863 | |
| dc.description.abstract | This report provides an overview of the Invited Session titled "Computer Science for Green Technologies and Sustainable Development", held during the 27th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems from September 6–8, 2023, in Athens, Greece. The session was co-chaired by Adam Sulich and Tomasz Zema, with additional organizational support from Letycja Sołoducho-Pelc. The session requirement was attendance by participants in person. The purpose of this report is to summarize the papers presented and the discussions that took place within the session. Therefore, this paper has a descriptive approach and does not attempt to combine the presented papers. | en |
| dc.language.iso | en | |
| dc.publisher | Wydawnictwo Uniwersytetu Łódzkiego | pl |
| dc.relation.ispartofseries | Władza Sądzenia;25 | pl |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0 | |
| dc.title | Computer Science for Green Technologies and Sustainable Development: Invited session Report at KES 2023 | en |
| dc.type | Other | |
| dc.page.number | 156-163 | |
| dc.contributor.authorAffiliation | Wroclaw University of Economics and Business | en |
| dc.identifier.eissn | 2300-1690 | |
| dc.references | Cavojsky, M., & Drozda, M. (2023). Search by Pattern in GPS Trajectories. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 495 LNICST, 117–132. https://doi.org/10.1007/978-3-031-31891-7_9 | en |
| dc.references | Franczyk, B., Hernes, M., Kozierkiewicz, A., Kozina, A., Pietranik, M., Roemer, I., & Schieck, M. (2020). Deep learning for grape variety recognition. Procedia Computer Science, 176, 1211–1220. https://doi.org/10.1016/j.procs.2020.09.117 | en |
| dc.references | Jakkaladiki, S. P., Janečková, M., Krunčík, J., Malý, F., & Otčenášková, T. (2023). Deep learning-based education decision support system for student E-learning performance prediction. Scalable Computing Practice and Experience, 24(3), 327–338. https://doi.org/10.12694/scpe.v24i3.2188 | en |
| dc.references | Kozar, Ł. (2017). Environmental risk management in the enterprise as a way to support the development of green economy. Prace Naukowe Uniwersytetu Ekonomicznego We Wrocławiu, 470, 62–74. https://doi.org/10.15611/pn.2017.470.06 | en |
| dc.references | Kozar, Ł., & Oleksiak, P. (2022). Organizacje wobec wyzwań zrównoważonego rozwoju – wybrane aspekty. Wydawnictwo Uniwersytetu Łódzkiego. https://doi.org/10.18778/8220-819-1 | en |
| dc.references | Lewoc, J. B., Izworski, A., Skowronski, S. F., Kieleczawa, A., Hersh, M., & Chomiak-Orsa, I. (2015). Engineering ethics problems in a developing country. W Ethical Engineering for International Development and Environmental Sustainability. Springer-Verlag London Ltd. https://doi.org/10.1007/978-1-4471-6618-4_8 | en |
| dc.references | Markowska, A., Krzywonos, M., Čuljak, M., Walaszczyk, E., Miałkowska, K., Chojnacka-Komorowska, A., Matouk, K., & Śnierzyński, M. (2022). Machine learning for environmental life cycle costing. Procedia Computer Science, 207, 4087–4096. https://doi.org/10.1016/j.procs.2022.09.471 | en |
| dc.references | Martusewicz, J., Szewczyk, K., & Wierzbic, A. (2022). The Environmental Protection and Effective Energy Consumption in the Light of the EFQM Model 2020 – Case Study. Energies, 15(19), 1–17. https://doi.org/10.3390/en15197260 | en |
| dc.references | Sulich, A., & Sołoducho-Pelc, L. (2022). The circular economy and the Green Jobs creation. Environmental Science and Pollution Research, 29(10), 14231–14247. https://doi.org/10.1007/s11356-021-16562-y | en |
| dc.references | Sulich, A., & Zema, T. (2023). Green energy transition in Germany: A bibliometric study. Forum Scientiae Oeconomia, 11(2), 175–195. https://doi.org/10.23762/FSO_VOL11_NO2_9 | en |
| dc.references | Zema, T., Kozina, A., Sulich, A., Römer, I., & Schieck, M. (2022). Deep learning and forecasting in practice: an alternative costs case. Procedia Computer Science, 207, 2958–2967. https://doi.org/10.1016/j.procs.2022.09.354 | en |
| dc.references | Zema, T., Sulich, A., & Kulhanek, L. (2023). Energy sales forecasting in a sustainable development context: bibliometric review. W Z. Nedelko & R. Korez-Vide (Red.), 7th FEB International Scientific Conference: Strengthening Resilience by Sustainable Economy and Business – Towards the SDGs (ss. 99–108). University of Maribor. https://doi.org/https://doi.org/10.18690/um.epf.3.2023 | en |
| dc.contributor.authorEmail | tomasz.zema@ue.wroc.pl | |
| dc.identifier.doi | 10.18778/2300-1690.25.10 |
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