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Found 8 papers, 0 papers with code
Learnable WSN Deployment of Evidential Collaborative Sensing Model
A learnable sensor deployment network (LSDNet) considering both sensor contribution and detection capability, is proposed for achieving the optimal deployment of WSNs.
Random Graph Set and Evidence Pattern Reasoning Model
Therefore, Random Graph Set (RGS) were proposed to model complex relationships and represent more event types.
Differential Convolutional Fuzzy Time Series Forecasting
Thanks to the learnable ability of the neural network, the length of fuzzy rules established in FTSF is expended to an arbitrary length that the expert is not able to handle by the expert system.
DVS: Deep Visibility Series and its Application in Construction Cost Index Forecasting
Visibility Graph (VG) algorithm is used for time series forecasting in previous research, but the forecasting effect is not as good as deep learning prediction methods such as methods based on Artificial Neural Network (ANN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM).
Construction Cost Index Forecasting: A Multi-feature Fusion Approach
The main contribution of MFF is to improve the prediction accuracy of CCI, and propose a feature fusion framework for time series.
Uncertainty Measurement of Basic Probability Assignment Integrity Based on Approximate Entropy in Evidence Theory
The main contribution of paper is to define the integrity of the basic probability assignment then the approximate entropy of the BPA is proposed to measure the uncertainty of the integrity of the BPA.
A Fast Evidential Approach for Stock Forecasting
In this paper, the fusion method of evidence theory is applied to stock price prediction.
A novel weighted approach for time series forecasting based on visibility graph
Firstly, the time series will be transformed into a complex network, and the similarity between nodes will be found.
