Zobrazeno 1 - 10
of 717
pro vyhledávání: '"drift detection"'
Autor:
Chamod Samarajeewa, Daswin De Silva, Milos Manic, Nishan Mills, Harsha Moraliyage, Damminda Alahakoon, Andrew Jennings
Publikováno v:
Energy and AI, Vol 17, Iss , Pp 100403- (2024)
Accurate energy consumption forecasting is crucial for reducing operational costs, achieving net-zero carbon emissions, and ensuring sustainable buildings and cities of the future. Despite the frequent use of Artificial Intelligence (AI) algorithms f
Externí odkaz:
https://doaj.org/article/325118716e16446f8b0ab543d24f29d9
Publikováno v:
Frontiers in Artificial Intelligence, Vol 7 (2024)
In an increasing number of industrial and technical processes, machine learning-based systems are being entrusted with supervision tasks. While they have been successfully utilized in many application areas, they frequently are not able to generalize
Externí odkaz:
https://doaj.org/article/208cd65f0611459abe2af379a0b0f580
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 131, Iss , Pp 103937- (2024)
This paper presents an advanced method for the reliable detection of failures and online calibration of airborne LiDARs and cameras in photogrammetric mapping of remote sensing imagery capture scenarios. Traditional calibration methods without target
Externí odkaz:
https://doaj.org/article/a4c5fa4e65af4958a6743a2942ebb5ea
Publikováno v:
Journal of Cloud Computing: Advances, Systems and Applications, Vol 13, Iss 1, Pp 1-19 (2024)
Abstract The detection of different types of concept drift has wide applications in the fields of cloud computing and security information detection. Concept drift detection can indeed assist in promptly identifying instances where model performance
Externí odkaz:
https://doaj.org/article/3861bf675e2642b49bc2910ac268d0c6
Publikováno v:
IEEE Access, Vol 12, Pp 80020-80034 (2024)
The adoption of cloud computing has been increasingly common across several industries in recent years and offers unparalleled flexibility and scalability in managing computational resources. However, the increasing reliance on cloud infrastructure a
Externí odkaz:
https://doaj.org/article/70db891ca15a4b058e250980b6396bf1
Publikováno v:
Frontiers in Artificial Intelligence, Vol 7 (2024)
The world surrounding us is subject to constant change. These changes, frequently described as concept drift, influence many industrial and technical processes. As they can lead to malfunctions and other anomalous behavior, which may be safety-critic
Externí odkaz:
https://doaj.org/article/08d5088818ab4d4b917b171c1cca9cc0
Publikováno v:
SoftwareX, Vol 26, Iss , Pp 101733- (2024)
Frouros is an open-source Python library capable of detecting drift in machine learning systems. It provides a combination of classical and more recent algorithms for drift detection, covering both concept and data drift. We have designed it to be co
Externí odkaz:
https://doaj.org/article/ae40bdbeacc34ee2b0cd2312c2687ffa
Publikováno v:
Frontiers in Marine Science, Vol 11 (2024)
Variations in Marine Dissolved Oxygen Concentrations (MDOC) play a critical role in the study of marine ecosystems and global climate evolution. Although artificial intelligence methods, represented by deep learning, can enhance the precision of MDOC
Externí odkaz:
https://doaj.org/article/4ba0ab727fea43aa83e326ea36df5930
Publikováno v:
PeerJ Computer Science, Vol 10, p e1827 (2024)
This study aims to investigate the problem of concept drift in cloud computing and emphasizes the importance of early detection for enabling optimum resource utilization and offering an effective solution. The analysis includes synthetic and real-wor
Externí odkaz:
https://doaj.org/article/846790e39d2440a38e486878efa642a0
Publikováno v:
Future Internet, Vol 16, Iss 3, p 94 (2024)
We develop the online process parameter design (OPPD) framework for efficiently handling streaming data collected from industrial automation equipment. This framework integrates online machine learning, concept drift detection and Bayesian optimizati
Externí odkaz:
https://doaj.org/article/ac9316bf1b97438a9fd05763c93b9fbd