Zobrazeno 1 - 10
of 681
pro vyhledávání: '"one-class SVM"'
Autor:
Gregorius Airlangga
Publikováno v:
Jurnal Lebesgue, Vol 5, Iss 1, Pp 49-61 (2024)
This study presents a comprehensive comparison of three machine learning algorithms for anomaly detection within seismic data, focusing on the unique geographical and geological context of Indonesia, a region prone to frequent seismic events. Local O
Externí odkaz:
https://doaj.org/article/cfdd438cd4b3481eb473465265fbfcf6
Publikováno v:
IEEE Access, Vol 12, Pp 104948-104963 (2024)
The smart grid environment, which emphasizes sustainability, dependability, and efficiency through smart components such as Intelligent Electronic Devices (IEDs), communication networks, and control systems, marks a revolutionary change in the way tr
Externí odkaz:
https://doaj.org/article/b5f5c3fb40584c95a174e0eda47127ea
Publikováno v:
Frontiers in Marine Science, Vol 10 (2024)
Plankton organisms are fundamental components of the earth’s ecosystem. Zooplankton feeds on phytoplankton and is predated by fish and other aquatic animals, being at the core of the aquatic food chain. On the other hand, Phytoplankton has a crucia
Externí odkaz:
https://doaj.org/article/188cd3c4c1bd4fa796a6b3a64f5dbdef
Publikováno v:
Applied Sciences, Vol 14, Iss 10, p 4318 (2024)
Today, Permanent Magnet Synchronous Motors (PMSMs) are a dominant choice in industry applications. During operation, different possible faults in the system can occur, so early and automated fault detection and severity estimation are required to ens
Externí odkaz:
https://doaj.org/article/eb40454bab184282aeccf297fd0297b5
Autor:
Imane Zidaoui, Cédric Wemmert, Matthieu Dufresne, Claude Joannis, Sandra Isel, Jonathan Wertel, José Vazquez
Publikováno v:
Water Science and Technology, Vol 87, Iss 12, Pp 2957-2970 (2023)
To prevent the pollution of water resources, the measurement and the limitation of wastewater discharges are required. Despite the progress in the field of data acquisition systems, sensors are subject to malfunctions that can bias the evaluation of
Externí odkaz:
https://doaj.org/article/c5aa0152038b46be84990845ec8e1bd3
Publikováno v:
IEEE Access, Vol 11, Pp 128106-128124 (2023)
One of the powerful techniques in data modeling is accounting for features that are available at the training stage, but are not available when the trained model is used to classify or predict test data — Learning Using Privileged Information parad
Externí odkaz:
https://doaj.org/article/ae03a9438158478cbe31f22b5816e50b
Publikováno v:
Animals, Vol 14, Iss 2, p 281 (2024)
Managing the risk of injury or illness is an important consideration when keeping pets. This risk can be minimized if pets are monitored on a regular basis, but this can be difficult and time-consuming. However, because only the external behavior of
Externí odkaz:
https://doaj.org/article/e6f0710cfbf345548580f1dbbdb72358
Publikováno v:
Frontiers in Earth Science, Vol 10 (2023)
The accuracy of data-driven landslide susceptibility mapping (LSM) is closely affected by the quality of non-landslide samples. This research proposes a method combining a self-organizing-map (SOM) and a one-class SVM (SOM-OCSVM) to generate more rea
Externí odkaz:
https://doaj.org/article/7e4747b44988472c8485c4be17f97ec7
Publikováno v:
Jisuanji kexue, Vol 49, Iss 3, Pp 144-151 (2022)
Large-scale high-dimensional unbalanced data handling is a major challenge in anomaly detection.One-class support vector machine(OCSVM) is very efficient at handling unbalanced data,but it is not suitable for large-scale high-dimensional dataset.Mean
Externí odkaz:
https://doaj.org/article/d26cba94043b4401ae166085bde6da27
Autor:
Khanista Namee, Jantima Polpinij
Publikováno v:
Engineering and Applied Science Research, Vol 48, Iss 5, Pp 604-613 (2021)
Imbalanced sentiment is one of the key classification issues. Many studies have proposed imbalanced sentiment classification improvements, but the topic remains problematic as a major challenge. This paper proposes a method, called “concept-based o
Externí odkaz:
https://doaj.org/article/d5c826474c7d4460b86a8c716b9901e1