Computer Science for Green Technologies and Sustainable Development: Invited session Report at KES 2023

Autor

DOI:

https://doi.org/10.18778/2300-1690.25.10

Abstrakt

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.

Biogram autora

Tomasz Zema - Wroclaw University of Economics and Business

Tomasz Zema – doktorant Szkoły Doktorskiej na Uniwersytecie Ekonomicznym we Wrocławiu. Jego zainteresowania naukowe koncentrują się wokół zagadnień łączących uczenie maszynowe i metody prognozowania ze zrównoważonym rozwojem. Pasjonat fotografii, przyrody i podróży.

Bibliografia

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
Zobacz w Google Scholar DOI: https://doi.org/10.1007/978-3-031-31891-7_9

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
Zobacz w Google Scholar DOI: https://doi.org/10.1016/j.procs.2020.09.117

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
Zobacz w Google Scholar DOI: https://doi.org/10.12694/scpe.v24i3.2188

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
Zobacz w Google Scholar DOI: https://doi.org/10.15611/pn.2017.470.06

Kozar, Ł., & Oleksiak, P. (2022). Organizacje wobec wyzwań zrównoważonego rozwoju – wybrane aspekty. Wydawnictwo Uniwersytetu Łódzkiego. https://doi.org/10.18778/8220-819-1
Zobacz w Google Scholar DOI: https://doi.org/10.18778/8220-819-1

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
Zobacz w Google Scholar DOI: https://doi.org/10.1007/978-1-4471-6618-4_8

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
Zobacz w Google Scholar DOI: https://doi.org/10.1016/j.procs.2022.09.471

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
Zobacz w Google Scholar DOI: https://doi.org/10.3390/en15197260

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
Zobacz w Google Scholar DOI: https://doi.org/10.1007/s11356-021-16562-y

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
Zobacz w Google Scholar

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
Zobacz w Google Scholar DOI: https://doi.org/10.1016/j.procs.2022.09.354

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
Zobacz w Google Scholar DOI: https://doi.org/10.18690/um.epf.3.2023.13

Pobrania

Opublikowane

2023-12-11

Jak cytować

Zema, T. (2023). Computer Science for Green Technologies and Sustainable Development: Invited session Report at KES 2023. Władza Sądzenia, (25), 156–163. https://doi.org/10.18778/2300-1690.25.10

Numer

Nr 25 (2023)

Dział

Other

Licencja

Creative Commons License

Utwór dostępny jest na licencji Creative Commons Uznanie autorstwa – Użycie niekomercyjne – Bez utworów zależnych 4.0 Międzynarodowe.