Szczegóły publikacji
Opis bibliograficzny
Artificial neural networks and machine learning as methods for improved air pollution control / Katarzyan SZRAMOWIAT-SALA // W: New insights into environmental and energy development [Dokument elektroniczny] : 4th international conference on Sustainable energy and environmental development : 13–15 October 2021, Krakow, Poland : extended abstracts / ed. Krzysztof Sornek. — Wersja do Windows. — Dane tekstowe. — Kraków : Institute of Sustainable Energy, 2021. — e-ISBN: 978-83-66559-07-3. — S. 34–39. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://www.instytutze.org/seed2021/download/publications/SEE... [2023-07-07]. — Bibliogr. s. 38–39, Abstr.
Autor
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 147657 |
|---|---|
| Data dodania do BaDAP | 2023-07-21 |
| Rok publikacji | 2021 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
Abstract
Progress in science and technology is observed almost everywhere in the world, and currently most of new advanced technical solutions are mainly aimed at protecting human lives, protecting the environment and facing the ecological effects of industrial development. Recently, advanced methods have been sought to improve and predict the processes that are processed while exhausts are produced and pollutants are released to the atmosphere. Artificial intelligence (AI) is more interesting among researchers, especially since it has already found its interest in almost every area of technology. At the same time, it is continuously improved to fully replace a human in the near future in such activities, such as cooking, telemarketing, or driving a car. In this work, the application possibility of artificial intelligence (artificial neural networks – ANN) has been reviewed in some environmental aspects. On the basis of the reviewed papers, it was concluded that the artificial intelligence, using the proper computational algorithms and matrices, may be successfully applied in air pollution control systems for the forecasting of emitted pollutants depending on chosen factors.