Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Georgios Leontidis"'
Autor:
Luke Farrow, Dominic Meek, Georgios Leontidis, Marion Campbell, Ewen Harrison, Lesley Anderson
Publikováno v:
Bone & Joint Research, Vol 13, Iss 9, Pp 507-512 (2024)
Despite the vast quantities of published artificial intelligence (AI) algorithms that target trauma and orthopaedic applications, very few progress to inform clinical practice. One key reason for this is the lack of a clear pathway from development t
Externí odkaz:
https://doaj.org/article/14238c62728b4c59922eef6e6dacc64a
Autor:
Matthew Beddows, Georgios Leontidis
Publikováno v:
Agriculture, Vol 14, Iss 6, p 883 (2024)
The importance of forecasting crop yields in agriculture cannot be overstated. The effects of yield forecasting are observed in all the aspects of the supply chain from staffing to supplier demand, food waste, and other business decisions. However, t
Externí odkaz:
https://doaj.org/article/b12221ccd111495990d3e078a5476d08
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 5, Iss 3, Pp 1055-1075 (2023)
Predicting emissions for gas turbines is critical for monitoring harmful pollutants being released into the atmosphere. In this study, we evaluate the performance of machine learning models for predicting emissions for gas turbines. We compared an ex
Externí odkaz:
https://doaj.org/article/c60dbc9874b34e8eb8f721076c1f8ac7
Autor:
Simon Pearson, Steve Brewer, Louise Manning, Luc Bidaut, George Onoufriou, Aiden Durrant, Georgios Leontidis, Charbel Jabbour, Andrea Zisman, Gerard Parr, Jeremy Frey, Roger Maull
Publikováno v:
Frontiers in Sustainable Food Systems, Vol 7 (2023)
The food system is undergoing a digital transformation that connects local and global supply chains to address economic, environmental, and societal drivers. Digitalisation enables firms to meet sustainable development goals (SDGs), address climate c
Externí odkaz:
https://doaj.org/article/4caf88cf02764ca0b7aebf35238eb666
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 3, Iss 4, Pp 819-834 (2021)
Fully Homomorphic Encryption (FHE) is a relatively recent advancement in the field of privacy-preserving technologies. FHE allows for the arbitrary depth computation of both addition and multiplication, and thus the application of abelian/polynomial
Externí odkaz:
https://doaj.org/article/fa94bfc18c224f63bdcff64d0c94f133
Publikováno v:
Sensors, Vol 22, Iss 21, p 8124 (2022)
We present automatically parameterised Fully Homomorphic Encryption (FHE) for encrypted neural network inference and exemplify our inference over FHE-compatible neural networks with our own open-source framework and reproducible examples. We use the
Externí odkaz:
https://doaj.org/article/037773df58094234af43da2471582f1e
Autor:
Georgios Leontidis, Bashar Alhnaity, Simon Pearson, Bert Schamp, Shouyong Jiang, Stefanos Kollias
Publikováno v:
Information Sciences. 560:35-50
Multi-step-ahead prediction is considered of major significance for time series analysis in many real life problems. Existing methods mainly focus on one-step-ahead forecasting, since multiple step forecasting generally fails due to accumulation of p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::048f32b11eb5af19e7ab943f2aff57e4
https://doi.org/10.1016/j.ins.2021.01.037
https://doi.org/10.1016/j.ins.2021.01.037
Publikováno v:
Computers and Electronics in Agriculture. 208:107784
Yield forecasting is a critical first step necessary for yield optimisation, with important consequences for the broader food supply chain, procurement, price-negotiation, logistics, and supply. However yield forecasting is notoriously difficult, and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::11f315f1aecb826e8e74440f0b3f05d8
https://doi.org/10.1016/j.compag.2023.107784
https://doi.org/10.1016/j.compag.2023.107784
The management of water resource systems is a longstanding and inherently complex problem, balancing an increasing number of interests to meet short- and long-term objectives sustainably. The difficulty of analyzing large-scale, multi-reservoir water
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c9e6590fa18590eb8be93f69b46d88c7
https://doi.org/10.5194/egusphere-egu22-3946
https://doi.org/10.5194/egusphere-egu22-3946
Publikováno v:
Acta Horticulturae. :425-432
Funding Information: This work is part of EU Interreg SMARTGREEN project (2017-2021). We would like to thank all the growers (UK & EU), for providing the data. Their valuable feedback, suggestions and comments are highly appreciated to increase the o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5a2ca7b2f82215954e2fdd25e06e3d17
https://doi.org/10.17660/actahortic.2020.1296.55
https://doi.org/10.17660/actahortic.2020.1296.55