Meng Chen
Shandong University
27 Papers
136 Citations
Meng Chen is an academic researcher from Shandong University. The author has contributed to research in topics: Computer science & Markov model. The author has an hindex of 8, co-authored 27 publications. Previous affiliations of Meng Chen include York University.
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Papers
PCNN: Deep Convolutional Networks for Short-Term Traffic Congestion Prediction
Meng Chen,Xiaohui Yu,Yang Liu +2 more
TL;DR: A novel method named PCNN is proposed, which is based on a deep convolutional neural network, modeling periodic traffic data for short-term traffic congestion prediction, and experimental results on a real-world urban traffic data set confirm that folding time series data into a 2-D matrix is effective and PCNN outperforms the baselines significantly for the task of short- term congestion prediction.
Normality Learning in Multispace for Video Anomaly Detection
TL;DR: A semi-supervised method based on the generative adversarial network and frame prediction, wherein the normality is learned in both the original image space and latent space, and the events deviating from thenormality are detected as anomalies.
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NLPMM: A Next Location Predictor with Markov Modeling
Meng Chen,Yang Liu,Xiaohui Yu,Xiaohui Yu +3 more
- 13 May 2014
TL;DR: In this paper, a Next Location Predictor with Markov Modeling (NLPMM) is proposed to predict the next locations of the moving objects with a historical dataset of trajectories.
87
Eating healthier: Exploring nutrition information for healthier recipe recommendation
TL;DR: A novel framework named NutRec is proposed, which models the interactions between ingredients and their proportions within recipes for the purpose of offering healthy recommendation and the empirical results support the framework’s intuition and showcase its ability to retrieve healthier recipes.
Missing data imputation for traffic congestion data based on joint matrix factorization
TL;DR: Experimental results on a real traffic dataset indicate that modeling the three features of congestion patterns simultaneously is effective and CIM outperforms the baselines for the task of missing traffic data imputation.
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