Zobrazeno 1 - 10
of 371
pro vyhledávání: '"Anastasios Doulamis"'
Autor:
Sotirios Athanasoulias, Fernanda Guasselli, Nikolaos Doulamis, Anastasios Doulamis, Nikolaos Ipiotis, Athina Katsari, Lina Stankovic, Vladimir Stankovic
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-17 (2024)
Abstract The growing availability of smart meter data has facilitated the development of energy-saving services like demand response, personalized energy feedback, and non-intrusive-load-monitoring applications, all of which heavily rely on advanced
Externí odkaz:
https://doaj.org/article/e94b65e7fd94474cb3a452acb8aea082
Autor:
Anastasios Temenos, Athanasios Voulodimos, Vera Korelidou, Athanasios Gelasakis, Dimitrios Kalogeras, Anastasios Doulamis, Nikolaos Doulamis
Publikováno v:
Journal of Agriculture and Food Research, Vol 16, Iss , Pp 101174- (2024)
Modern livestock farming systems face the challenge of meeting the growing demand for dairy and meat products while ensuring the well-being of animals. Body Condition Scoring serves as a vital process for assessing the body reserves in animals, impac
Externí odkaz:
https://doaj.org/article/5ee47ff7af4a44159d21ea63b511b22f
Autor:
Sotirios Athanasoulias, Fernanda Guasselli, Nikolaos Doulamis, Anastasios Doulamis, Nikolaos Ipiotis, Athina Katsari, Lina Stankovic, Vladimir Stankovic
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-1 (2024)
Externí odkaz:
https://doaj.org/article/ce9658e5e1db4b94a5aeb29265e1210c
Autor:
Ioannis Georgoulas, Eftychios Protopapadakis, Konstantinos Makantasis, Dylan Seychell, Anastasios Doulamis, Nikolaos Doulamis
Publikováno v:
IEEE Access, Vol 11, Pp 124819-124832 (2023)
Hyperspectral data classification is one of the fundamental problems in remote sensing. Several algorithms based on supervised machine learning have been proposed to address it. The performance, however, of the proposed algorithms is inherently depen
Externí odkaz:
https://doaj.org/article/9df9e7a8f16e429f91e204b0dec9bbae
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 6969-6979 (2023)
Knowing the actual precipitation in space and time is critical in hydrological modeling applications, yet the spatial coverage with rain gauge stations is limited due to economic constraints. Gridded satellite precipitation datasets offer an alternat
Externí odkaz:
https://doaj.org/article/b11a1bcf6afd47a8b17bb7320af8316e
Autor:
George Kopsiaftis, Maria Kaselimi, Eftychios Protopapadakis, Athanasios Voulodimos, Anastasios Doulamis, Nikolaos Doulamis, Aristotelis Mantoglou
Publikováno v:
Frontiers in Water, Vol 5 (2023)
In this work we investigate the performance of various lower-fidelity models of seawater intrusion in coastal aquifer management problems. The variable density model is considered as the high-fidelity model and a pumping optimization framework is app
Externí odkaz:
https://doaj.org/article/01e5a6a5644e4afb8caebb870339b063
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 3, p 035044 (2024)
Merging satellite and gauge data with machine learning produces high-resolution precipitation datasets, but uncertainty estimates are often missing. We addressed the gap of how to optimally provide such estimates by benchmarking six algorithms, mostl
Externí odkaz:
https://doaj.org/article/23cdddd51805445bb5233f0473593de4
Publikováno v:
Remote Sensing, Vol 15, Iss 20, p 4912 (2023)
Regression algorithms are regularly used for improving the accuracy of satellite precipitation products. In this context, satellite precipitation and topography data are the predictor variables, and gauged-measured precipitation data are the dependen
Externí odkaz:
https://doaj.org/article/ebafb1eacd914249bdd3018eb01fbee7
Publikováno v:
Frontiers in Physiology, Vol 13 (2022)
Diabetic foot complications have multiple adverse effects in a person’s quality of life. Yet, efficient monitoring schemes can mitigate or postpone any disorders, mainly by early detecting regions of interest. Nowadays, optical sensors and artifici
Externí odkaz:
https://doaj.org/article/062b660bdf5c407f8bb7f7fae07f4808
Autor:
Konstantinos Makantasis, Alexandros Georgogiannis, Athanasios Voulodimos, Ioannis Georgoulas, Anastasios Doulamis, Nikolaos Doulamis
Publikováno v:
IEEE Access, Vol 9, Pp 58609-58620 (2021)
An increasing number of emerging applications in data science and engineering are based on multidimensional and structurally rich data. The irregularities, however, of high-dimensional data often compromise the effectiveness of standard machine learn
Externí odkaz:
https://doaj.org/article/2a6eefc9a37545c8b561cea888e1e7c5