Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Serkan Kartal"'
Publikováno v:
Pamukkale University Journal of Engineering Sciences, Vol 24, Iss 5, Pp 857-863 (2018)
Genelleştirilmiş Regresyon Yapay Sinir Ağı (GRYSA) radyal tabanlı çalışan ve genellikle tahminleyici olarak kullanılan denetimli-öğrenimli bir yapay sinir ağı (YSA) modelidir. Kolay modellenebilmesinin yanında hızlı ve tutarlı sonuç
Externí odkaz:
https://doaj.org/article/c7236eb3314b41898824f6fae06d9472
Publikováno v:
Pamukkale University Journal of Engineering Sciences, Vol 24, Iss 5, Pp 857-863 (2018)
Genelleştirilmiş Regresyon Yapay Sinir Ağı (GRYSA) radyal tabanlı çalışan ve genellikle tahminleyici olarak kullanılan denetimli-öğrenimli bir yapay sinir ağı (YSA) modelidir. Kolay modellenebilmesinin yanında hızlı ve tutarlı sonuç
Externí odkaz:
https://doaj.org/article/a4693978c50f45abbfa2f74c3958c65d
Autor:
Serkan Kartal, Sunita Choudhary, Jan Masner, Jana Kholová, Michal Stočes, Priyanka Gattu, Stefan Schwartz, Ewaut Kissel
Publikováno v:
Sensors, Vol 21, Iss 23, p 8022 (2021)
This study tested whether machine learning (ML) methods can effectively separate individual plants from complex 3D canopy laser scans as a prerequisite to analyzing particular plant features. For this, we scanned mung bean and chickpea crops with Pla
Externí odkaz:
https://doaj.org/article/be5bb2fb943545fca868cac803dfa73d
Autor:
Enis ARSLAN, Serkan KARTAL
Publikováno v:
Turkish Journal of Remote Sensing and GIS. :100-113
Generation of flood inundation maps is beneficial in flood risk assessment and evaluation. Flood inundation mapping can be achieved by many remote sensing techniques like change detection (CD) with thresholding and machine learning-based (ML) methods
Autor:
Serkan KARTAL
Publikováno v:
Çukurova Üniversitesi Mühendislik Fakültesi Dergisi. :853-862
Uzaktan algılama çalışmalarında uydu görüntülerindeki eksik verilerin yeniden yapılandırılması, veri kullanılabilirliğini artırmak ve analiz süreçlerini kolaylaştırmak açısından büyük önem taşımaktadır. Bu çalışmada, bu
Autor:
Serkan Kartal, Aliihsan Sekertekin
Publikováno v:
Environmental Science and Pollution Research. 29:67115-67134
Land surface temperature (LST) prediction is of great importance for climate change, ecology, environmental and industrial studies. These studies require accurate LST map predictions considering both spatial and temporal dynamics. In this study, mult
Peak wind gust (Wp) is a crucial meteorological variable for wind farm planning and operations. However, for many wind farm sites, there is a dearth of on-site measurements of (Wp). In this paper, we propose a machine-learning approach (called INTRIG
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6e0a99f5aae6ebeac0bc98b5d37248ad
https://doi.org/10.5194/wes-2023-30
https://doi.org/10.5194/wes-2023-30
Autor:
Serkan Kartal
Publikováno v:
Engineering Applications of Artificial Intelligence, 118
Spatiotemporal time series prediction plays a crucial role in a wide range of applications. However, in most of the studies, spatial information was ignored and predictions were carried out either on a few points or on average values. In this study,
Autor:
Nagham Alhawas, Serkan Kartal
Publikováno v:
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ISBN: 9783031063701
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b2945a3a41bbc44ddb1db8121d745b0a
https://doi.org/10.1007/978-3-031-06371-8_38
https://doi.org/10.1007/978-3-031-06371-8_38
Autor:
Sunita Choudhary, Jan Masner, Stefan Schwartz, Serkan Kartal, Jana Kholova, Priyanka Gattu, Michal Stočes, Ewaut Kissel
Publikováno v:
Sensors; Volume 21; Issue 23; Pages: 8022
Sensors (Basel, Switzerland)
Sensors, Vol 21, Iss 8022, p 8022 (2021)
Sensors (Basel, Switzerland)
Sensors, Vol 21, Iss 8022, p 8022 (2021)
This study tested whether machine learning (ML) methods can effectively separate individual plants from complex 3D canopy laser scans as a prerequisite to analyzing particular plant features. For this, we scanned mung bean and chickpea crops with Pla