Zobrazeno 1 - 10
of 112
pro vyhledávání: '"Xia Li Wang"'
Autor:
Yi-fang He, Xia-li Wang, Shuang-ping Deng, Yan-li Wang, Qing-qing Huang, Shu Lin, Guo-rong Lyu
Publikováno v:
Frontiers in Endocrinology, Vol 14 (2023)
Stem cells have self-renewal, replication, and multidirectional differentiation potential, while progenitor cells are undifferentiated, pluripotent or specialized stem cells. Stem/progenitor cells secrete various factors, such as cytokines, exosomes,
Externí odkaz:
https://doaj.org/article/3d34b84b91344d7aa16ae83d6485f22a
Publikováno v:
Insights into Imaging, Vol 13, Iss 1, Pp 1-15 (2022)
Abstract Changes in tissue stiffness by physiological or pathological factors in tissue structure are identified earlier than their clinical features. Pathological processes such as uterine fibrosis, adenomyosis, endometrial lesions, infertility, and
Externí odkaz:
https://doaj.org/article/e0ecb66fcacd4f59bee782d2d486ecbf
Publikováno v:
Acta Obstetricia et Gynecologica Scandinavica. 102:657-668
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c1abfa1775e94c284e818ddaccf4289b
https://doi.org/10.1111/aogs.14580
https://doi.org/10.1111/aogs.14580
Autor:
Xiaochun Wang, Xia Li Wang
Publikováno v:
Machine Learning and Data Mining in Pattern Recognition ISBN: 9783319961323
MLDM (2)
MLDM (2)
Efficient Euclidean minimum spanning tree algorithms have been proposed for large scale datasets which run typically in time near linear in the size of the data but may not usually be feasible for high-dimensional data. For data consisting of sparse
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::68c9d54ac61106762e1e86cbfebc0fe4
https://doi.org/10.1007/978-3-319-96133-0_11
https://doi.org/10.1007/978-3-319-96133-0_11
Publikováno v:
Machine Learning and Data Mining in Pattern Recognition ISBN: 9783319961354
MLDM (1)
MLDM (1)
Outlier detection is an important task in data mining and has high practical value in numerous applications such as astronomical observation, text detection, fraud detection and so on. At present, a large number of popular outlier detection algorithm
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::df6b2f1e027cd3ebf14fccaf3974318b
https://doi.org/10.1007/978-3-319-96136-1_31
https://doi.org/10.1007/978-3-319-96136-1_31
Publikováno v:
Machine Learning and Data Mining in Pattern Recognition ISBN: 9783319961323
MLDM (2)
MLDM (2)
Euclidean minimum spanning tree algorithms run typically with quadratic computational complexity, which is not practical for large scale high dimensional datasets. In this paper, we propose a new two-level approximate Euclidean minimum spanning tree
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9304a96ed339ad2789839070d941aa45
https://doi.org/10.1007/978-3-319-96133-0_21
https://doi.org/10.1007/978-3-319-96133-0_21
Publikováno v:
2017 International Conference on Computational Science and Computational Intelligence (CSCI).
Outlier detection refers to detecting patterns that do not conform to an established normal behavior in a given data set and has found immense use in a wide variety of practical domains. Due to its important applications, many techniques have been de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::91cd316f07c145509fb62f8a10e4dc02
https://doi.org/10.1109/csci.2017.247
https://doi.org/10.1109/csci.2017.247
Publikováno v:
2017 International Conference on Computational Science and Computational Intelligence (CSCI).
Density-based clustering algorithms are well known for identifying clusters possessing very different local densities and existing in different regions of data space. However, the parameters required by most popular density-based clustering algorithm
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8a91761c8afd2c56d5257951ddd4e347
https://doi.org/10.1109/csci.2017.132
https://doi.org/10.1109/csci.2017.132
Publikováno v:
2017 International Conference on Computational Science and Computational Intelligence (CSCI).
Clustering aims at grouping a given set of data points into a number of clusters without resorting to any a priori knowledge. Due to its important applications in data mining, many techniques have been developed for clustering. Being one of the most
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dd9964fd6591c08384b4d65e61b898eb
https://doi.org/10.1109/csci.2017.68
https://doi.org/10.1109/csci.2017.68
Publikováno v:
Information Systems. 48:89-112
Today's real-world databases typically contain millions of items with many thousands of fields. As a result, traditional distribution-based outlier detection techniques have more and more restricted capabilities and novel k-nearest neighbors based ap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6f37ea824df022a2adf8f0cf8d5f210a
https://doi.org/10.1016/j.is.2014.09.002
https://doi.org/10.1016/j.is.2014.09.002