Editorial: Fuzzy decisions and machine learning methods in climate change
| dc.authorid | 0000-0002-9858-1266 | |
| dc.authorid | 0000-0002-8072-031X | |
| dc.contributor.author | Yüksel, Serhat | |
| dc.contributor.author | Dinçer, Hasan | |
| dc.contributor.author | Mikhaylov, Alexey | |
| dc.date.accessioned | 2023-08-08T06:32:54Z | |
| dc.date.available | 2023-08-08T06:32:54Z | |
| dc.date.issued | 2023 | |
| dc.department | İstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Uluslararası Ticaret ve Finansman Bölümü | |
| dc.description.abstract | Different factors can affect Fuzzy decisions and machine learning methods in climate change. Energy efficiency ensures that energy resources are used more effectively, which means energy savings. Less energy consumption reduces energy costs and ensures that energy sources can be used for a longer period. System quality is very important for ensuring Fuzzy decisions and machine learning methods in climate change. Accurate and reliable data is needed for Fuzzy decisions and machine learning methods in climate change. Energy consumption, energy costs and other performance indicators must be accurately measured and recorded. Quality systems reliably perform data collection, automation, and measurement, ensuring the precision and accuracy of data. To ensure Fuzzy Decisions and Machine Learning Methods in Climate Change, effective legal regulations should also be provided. Energy performance regulations help set energy efficiency standards and targets. These standards and targets encourage government and organizations to achieve a certain level of energy efficiency. | |
| dc.identifier.citation | Yüksel, S., Dinçer, H. ve Mikhaylov, A. (2023). Editorial: Fuzzy decisions and machine learning methods in climate change. Frontiers in Environmental Science, 11. https://dx.doi.org/10.3389/fenvs.2023.1235845 | |
| dc.identifier.doi | 10.3389/fenvs.2023.1235845 | |
| dc.identifier.issn | 2296-665X | |
| dc.identifier.scopus | 2-s2.0-85164940405 | |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.uri | https://dx.doi.org/10.3389/fenvs.2023.1235845 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12511/11281 | |
| dc.identifier.volume | 11 | |
| dc.identifier.wos | 001027943200001 | en_US |
| dc.identifier.wosquality | Q2 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.institutionauthor | Yüksel, Serhat | |
| dc.institutionauthor | Dinçer, Hasan | |
| dc.language.iso | en | |
| dc.publisher | Frontiers Media SA | |
| dc.relation.ispartof | Frontiers in Environmental Science | en_US |
| dc.relation.publicationcategory | Diğer | |
| dc.rights | Attribution 4.0 International | * |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | Renewable Energy | |
| dc.subject | Economic Growth | |
| dc.subject | Emission | |
| dc.subject | Emerging Economies | |
| dc.subject | Model | |
| dc.title | Editorial: Fuzzy decisions and machine learning methods in climate change | |
| dc.type | Editorial |
