Zobrazeno 1 - 10
of 163
pro vyhledávání: '"Jinli Cao"'
Publikováno v:
Data Science and Engineering, Vol 8, Iss 1, Pp 23-35 (2023)
Abstract With the rapid development of social networks, academic social networks have attracted increasing attention. In particular, providing personalized recommendations for learners considering data sparseness and cold-start scenarios is a challen
Externí odkaz:
https://doaj.org/article/8271b2aec903444eae53546923d49d0b
Publikováno v:
IEEE Access, Vol 4, Pp 6554-6566 (2016)
Epilepsy detection from electrical characteristics of EEG signals obtained from the brain of undergone subject is a challenge task for both research and neurologist due to the non-stationary and chaotic nature of EEG signals. As epileptic EEG signals
Externí odkaz:
https://doaj.org/article/43e411fd036b49d5ad918eb65e998605
Publikováno v:
Applied Sciences, Vol 10, Iss 2, p 606 (2020)
A data stream can be considered as a sequence of examples that arrive continuously and are potentially unbounded, such as web page visits, sensor readings and call records. One of the serious and challenging problems that appears in a data stream is
Externí odkaz:
https://doaj.org/article/c291229acb6e44b4bc78923b301c20f2
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 11, Iss 1 (2018)
This paper presents a computer aided analysis system for detecting epileptic seizure from electroencephalogram (EEG) signal data. As EEG recordings contain a vast amount of data, which is heterogeneous with respect to a time-period, we intend to intr
Externí odkaz:
https://doaj.org/article/f80315eae6b54179901d945f34712944
Publikováno v:
Sensors, Vol 19, Iss 7, p 1489 (2019)
Hypertension is one of the most common cardiovascular diseases, which will cause severe complications if not treated in a timely way. Early and accurate identification of hypertension is essential to prevent the condition from deteriorating further.
Externí odkaz:
https://doaj.org/article/e72807c6e146402f8f82425eb3a4fd77
Publikováno v:
IEEE Transactions on Industrial Informatics. 19:5593-5601
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::75daed774a77917c62820bf54f3e13de
https://doi.org/10.1109/tii.2022.3192027
https://doi.org/10.1109/tii.2022.3192027
Publikováno v:
Information Sciences. 612:864-886
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::531905e2340d37c2ab631deb4a18328d
https://doi.org/10.1016/j.ins.2022.09.003
https://doi.org/10.1016/j.ins.2022.09.003
Publikováno v:
ACM Transactions on Knowledge Discovery from Data; Jan2024, Vol. 18 Issue 1, p1-23, 23p
Publikováno v:
World Wide Web. 26:827-848
Knowledge graph, as an extension of graph data structure, is being used in a wide range of areas as it can store interrelated data and reveal interlinked relationships between different objects within a large system. This paper proposes an algorithm
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2641354b0eef933f55ffe4d5039bfb55
https://doi.org/10.1007/s11280-022-01076-5
https://doi.org/10.1007/s11280-022-01076-5
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 34:2626-2640
Quasi-periodic time series (QTS) exists widely in the real world, and it is important to detect the anomalies of QTS. In this paper, we propose an automatic QTS anomaly detection framework (AQADF) consisting of a two-level clustering-based QTS segmen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::06f006dae87b02d983bbb90bb0a3a6ba
https://doi.org/10.1109/tkde.2020.3014806
https://doi.org/10.1109/tkde.2020.3014806