Zobrazeno 1 - 10
of 119
pro vyhledávání: '"Chi, Lianhua"'
Publikováno v:
JMIR Medical Informatics, Vol 9, Iss 4, p e25000 (2021)
BackgroundCardiovascular disease (CVD) is the greatest health problem in Australia, which kills more people than any other disease and incurs enormous costs for the health care system. In this study, we present a benchmark comparison of various artif
Externí odkaz:
https://doaj.org/article/948a5ea8f9b6420886bdbea1a5ff0699
Autor:
Zheng, Yu, Koh, Huan Yee, Jin, Ming, Chi, Lianhua, Wang, Haishuai, Phan, Khoa T., Chen, Yi-Ping Phoebe, Pan, Shirui, Xiang, Wei
The detection of anomalies in multivariate time series data is crucial for various practical applications, including smart power grids, traffic flow forecasting, and industrial process control. However, real-world time series data is usually not well
Externí odkaz:
http://arxiv.org/abs/2401.05800
Autor:
Zheng, Yu, Koh, Huan Yee, Jin, Ming, Chi, Lianhua, Phan, Khoa T., Pan, Shirui, Chen, Yi-Ping Phoebe, Xiang, Wei
Multivariate time-series anomaly detection is critically important in many applications, including retail, transportation, power grid, and water treatment plants. Existing approaches for this problem mostly employ either statistical models which cann
Externí odkaz:
http://arxiv.org/abs/2307.08390
Recently vision transformer models have become prominent models for a range of vision tasks. These models, however, are usually opaque with weak feature interpretability. Moreover, there is no method currently built for an intrinsically interpretable
Externí odkaz:
http://arxiv.org/abs/2207.05358
Publikováno v:
Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020: 2153-2156
In this work, we demonstrate a novel system, namely Web of Scholars, which integrates state-of-the-art mining techniques to search, mine, and visualize complex networks behind scholars in the field of Computer Science. Relying on the knowledge graph,
Externí odkaz:
http://arxiv.org/abs/2202.11311
Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce. Existing efforts in graph anomaly detection typically only consider the information in a single scale (view), th
Externí odkaz:
http://arxiv.org/abs/2202.05525
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021
Anomaly detection from graph data has drawn much attention due to its practical significance in many critical applications including cybersecurity, finance, and social networks. Existing data mining and machine learning methods are either shallow met
Externí odkaz:
http://arxiv.org/abs/2108.09896
Autor:
Zhao, Boxiang, Wang, Shuliang, Chi, Lianhua, Yuan, Hanning, Yuan, Ye, Li, Qi, Geng, Jing, Zhang, Shao-Liang
Publikováno v:
In Pattern Recognition April 2024 148
Publikováno v:
In Knowledge-Based Systems 28 February 2024 286
Publikováno v:
In Knowledge-Based Systems 4 November 2023 279