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Found 15 papers, 0 papers with code
EPL: Evidential Prototype Learning for Semi-supervised Medical Image Segmentation
Although current semi-supervised medical segmentation methods can achieve decent performance, they are still affected by the uncertainty in unlabeled data and model predictions, and there is currently a lack of effective strategies that can explore the uncertain aspects of both simultaneously.
Uncertainty-aware Evidential Fusion-based Learning for Semi-supervised Medical Image Segmentation
Although the existing uncertainty-based semi-supervised medical segmentation methods have achieved excellent performance, they usually only consider a single uncertainty evaluation, which often fails to solve the problem related to credibility completely.
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.
Time Series Forecasting via Semi-Asymmetric Convolutional Architecture with Global Atrous Sliding Window
The proposed model in this paper achieves state-of-the-art on most of time series datasets provided compared with competitive modern models.
GoogLe2Net: Going Transverse with Convolutions
Capturing feature information effectively is of great importance in vision tasks.
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.
Fortified quantum mass function utilizing ordinal pictorial check based on time interval analysis and expertise
Information management has enter a completely new era, quantum era.
MMGET: A Markov model for generalized evidence theory
In order to further embody the details of information and better conforms to situations of real world, a Markov model is introduced into the generalized evidence theory which helps extract complete information volume from evidence provided.
An approach utilizing negation of extended-dimensional vector of disposing mass for ordinal evidences combination in a fuzzy environment
In this paper, a detailed definition of ordinal frame of discernment has been provided.
Combining conflicting ordinal quantum evidences utilizing individual reliability
Therefore, in this paper, a specially designed method is designed to provide an excellent method which improves the combination of ordinal quantum evidences reasonably and reduce the effects brought by uncertainty contained in quantum information simultaneously.
Ordinal relative belief entropy
Therefore, a novel ordinal entropy to measure uncertainties of the frame of discernment considering the order of confirmation of propositions is proposed in this paper.
A new distance measure of Pythagorean fuzzy sets based on matrix and and its application in medical diagnosis
And some other kinds of methods neglect the relationship among three variables of pythagorean fuzzy set.
TDQMF: Two-dimensional quantum mass function
Quantum mass function has been applied in lots of fields because of its efficiency and validity of managing uncertainties in the form of quantum which can be regarded as an extension of classical Dempster-Shafer (D-S) evidence theory.
