Zobrazeno 1 - 10
of 188
pro vyhledávání: '"semi-supervised machine learning"'
Publikováno v:
Journal of Rock Mechanics and Geotechnical Engineering, Vol 16, Iss 10, Pp 4177-4191 (2024)
The accuracy of landslide susceptibility prediction (LSP) mainly depends on the precision of the landslide spatial position. However, the spatial position error of landslide survey is inevitable, resulting in considerable uncertainties in LSP modelin
Externí odkaz:
https://doaj.org/article/6b749188568245bb9b21322fedcdcbf7
Autor:
Shiwei Xu, Margaret E. Ackerman
Publikováno v:
BMC Bioinformatics, Vol 25, Iss 1, Pp 1-17 (2024)
Abstract Background Compared to traditional supervised machine learning approaches employing fully labeled samples, positive-unlabeled (PU) learning techniques aim to classify “unlabeled” samples based on a smaller proportion of known positive ex
Externí odkaz:
https://doaj.org/article/2a77ea4295814531885684129be5d940
Publikováno v:
Global Ecology and Conservation, Vol 53, Iss , Pp e03011- (2024)
Monitoring biodiversity over time and space is essential for effective conservation of habitats, processes, and dependent organisms. Estimating large abundances of individuals can be challenging (e.g. birds and mammals), demanding efficient and effec
Externí odkaz:
https://doaj.org/article/adf8b577f988417f876629e8193e1ad8
Autor:
Elie Neghawi, Yan Liu
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 5, Iss 4, Pp 1848-1876 (2023)
The rapid development of semi-supervised machine learning (SSML) algorithms has shown enhanced versatility, but pinpointing the primary influencing factors remains a challenge. Historically, deep neural networks (DNNs) have been used to underpin thes
Externí odkaz:
https://doaj.org/article/f368e196786d45878dfa8d0bd31ac012
Publikováno v:
Mathematics, Vol 12, Iss 16, p 2518 (2024)
Data analysis techniques can be powerful tools for rapidly analyzing data and extracting information that can be used in a latent space for categorizing observations between classes of data. Machine learning models that exploit learned data relations
Externí odkaz:
https://doaj.org/article/4432a901bc8342e198c9efe97ffdfa65
Autor:
Jordan R. Stomps, Paul P. H. Wilson, Kenneth J. Dayman, Michael J. Willis, James M. Ghawaly, Daniel E. Archer
Publikováno v:
Journal of Nuclear Engineering, Vol 4, Iss 3, Pp 448-466 (2023)
The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological m
Externí odkaz:
https://doaj.org/article/29a43967bdeb4baa88065c85e8652e66
Autor:
Mohammed G. Albayati, Jalal Faraj, Amy Thompson, Prathamesh Patil, Ravi Gorthala, Sanguthevar Rajasekaran
Publikováno v:
Big Data Mining and Analytics, Vol 6, Iss 2, Pp 170-184 (2023)
Most heating, ventilation, and air-conditioning (HVAC) systems operate with one or more faults that result in increased energy consumption and that could lead to system failure over time. Today, most building owners are performing reactive maintenanc
Externí odkaz:
https://doaj.org/article/1bf1ede38a0f4fc780b99a9c0ab8c9bc
Autor:
Kenneth David Strang
Publikováno v:
Big Data and Cognitive Computing, Vol 8, Iss 4, p 37 (2024)
A critical worldwide problem is that ransomware cyberattacks can be costly to organizations. Moreover, accidental employee cybercrime risk can be challenging to prevent, even by leveraging advanced computer science techniques. This exploratory projec
Externí odkaz:
https://doaj.org/article/a46b539a6cd04e84a99fa75a3e6e2d86
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