Results 51 to 60 of about 81,222 (134)
, 2011 We derive an analytical density functional for the single-site entanglement of the one-dimensional homogeneous Hubbard model, by means of an approximation to the linear entropy.
Irene D’Amico +2 more
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Examining CNN Representations with respect to Dataset Bias
, 2017 Given a pre-trained CNN without any testing samples, this paper proposes a simple yet effective method to diagnose feature representations of the CNN. We aim to discover representation flaws caused by potential dataset bias.
Wang, Wenguan +2 more
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Histopathological cancer images classification with Deng entropy
Biomedical Physics & Engineering ExpressAbstract Histopathological imaging is of paramount importance for the initial detection, diagnosis, and classification of tumors. Recurrent neural networks and convolutional neural networks have led to substantial advancements in digital pathology, thereby enhancing classification accuracy.
Elva Estrada-Estrada +2 more
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Measures of extended fractional Deng entropy and extropy with applications
Communications in Statistics - Simulation and ComputationCommunications in statistics / Simulation and computation (2024).
Nastaran Marzban Vaselabadi +3 more
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Long Short-Term Memory with Dynamic Skip Connections
, 2018 In recent years, long short-term memory (LSTM) has been successfully used to model sequential data of variable length. However, LSTM can still experience difficulty in capturing long-term dependencies.
Gong, Jingjing +6 more
core +1 more source
Deng Entropy: A Generalized Shannon Entropy To Measure Uncertainty
, 2015 Shannnon entropy is an efficient tool to measure uncertain information. However, it cannot handle the more uncertain situation when the uncertainty is represented by basic probability assignment (BPA), instead of probability distribution, under the framework of Dempster Shafer evidence theory. To address this issue, a new entropy, named as Deng entropy,
openaire +1 more source
An evidential sensor fusion method in fault diagnosis
Advances in Mechanical Engineering, 2016 Dempster–Shafer evidence theory is widely used in information fusion. However, it may lead to an unreasonable result when dealing with high conflict evidence.
Wen Jiang +3 more
doaj +1 more source
A new method to measure the divergence in evidential sensor data fusion
International Journal of Distributed Sensor Networks, 2019 Evidence theory is widely used in real applications such as target recognition because of its efficiency in evidential sensor data fusing. However, counter-intuitive results may be obtained in the situation when evidence highly conflicts with each other.
Yutong Song, Yong Deng
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Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory
Sensors, 2016 Sensor data fusion plays an important role in fault diagnosis. Dempster–Shafer (D-R) evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors.
Kaijuan Yuan +4 more
doaj +1 more source
Volume Dimension of Mass Functions in Complex Networks
MathematicsA novel definition of volume dimension for a mass function based on a sigmoid asymptote is proposed; in particular, we extend the volume dimension of a mass function to define the volume dimensions for nodes and edges in complex networks.
Maria del Carmen Soto-Camacho +3 more
doaj +1 more source