Anomaly detection based on weighted fuzzy-rough density
The density-based method is a more widely used anomaly detection. However, most of the
existing density-based methods mainly focus on dealing with certainty data and do not …
existing density-based methods mainly focus on dealing with certainty data and do not …
Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlook
As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for
sustainable development by harnessing the power of cross-domain data fusion from diverse …
sustainable development by harnessing the power of cross-domain data fusion from diverse …
SLAFusion: Attention fusion based on SAX and LSTM for dangerous driving behavior detection
Dangerous driving behaviors are the main cause of most traffic accidents, and the detection
of these behaviors is one of the extremely important researches in Intelligent Transportation …
of these behaviors is one of the extremely important researches in Intelligent Transportation …
A two-stage deep graph clustering method for identifying the evolutionary patterns of the time series of animation view counts
D He, Z Tang, Q Chen, Z Han, D Zhao, F Sun - Information Sciences, 2023 - Elsevier
Time-series clustering of view counts with changes in online time can identify animated
series with similar evolutionary count patterns over time, which may help companies reduce …
series with similar evolutionary count patterns over time, which may help companies reduce …
FedDAF: Federated deep attention fusion for dangerous driving behavior detection
Dangerous driving behavior detection is one of the most important researches in Intelligent
Transportation System (ITS), which can effectively reduce the probability and number of …
Transportation System (ITS), which can effectively reduce the probability and number of …
Integrating granular computing with density estimation for anomaly detection in high-dimensional heterogeneous data
Detecting anomalies in complex data is crucial for knowledge discovery and data mining
across a wide range of applications. While density-based methods are effective for handling …
across a wide range of applications. While density-based methods are effective for handling …
RFDANet: an FMCW and TOF radar fusion approach for driver activity recognition using multi-level attention based CNN and LSTM network
M Gu, K Chen, Z Chen - Complex & Intelligent Systems, 2024 - Springer
Dangerous driving behavior is a major contributing factor to road traffic accidents. Identifying
and intervening in drivers' unsafe driving behaviors is thus crucial for preventing accidents …
and intervening in drivers' unsafe driving behaviors is thus crucial for preventing accidents …
PrivShape: Extracting Shapes in Time Series under User-Level Local Differential Privacy
Time series have numerous applications in finance, healthcare, IoT, and smart city. In many
of these applications, time series typically contain personal data, so privacy infringement …
of these applications, time series typically contain personal data, so privacy infringement …
A novel Prophet model based on Gaussian linear fuzzy information granule for long-term time series prediction 1
H Yang, L Wang - Journal of Intelligent & Fuzzy Systems - content.iospress.com
The paper focuses on how to improve the prediction accuracy of time series and the
interpretability of prediction results. First, a novel Prophet model based on Gaussian linear …
interpretability of prediction results. First, a novel Prophet model based on Gaussian linear …
A multi-task learning approach for complex chemical processes based on manual predictive manipulating strategies
Z ZHANG, H LI, Y SHI - CIESC Journal, 2023 - hgxb.cip.com.cn
PID feedback control has been considered as the primary control strategy for chemical
production processes ever since. However, due to the large time-delay and nonlinearity of …
production processes ever since. However, due to the large time-delay and nonlinearity of …