Results 21 to 30 of about 3,693,066 (305)

An Extension to Deng’s Entropy in the Open World Assumption with an Application in Sensor Data Fusion

Sensors, 2018
Quantification of uncertain degree in the Dempster-Shafer evidence theory (DST) framework with belief entropy is still an open issue, even a blank field for the open world assumption.
Yongchuan Tang, Deyun Zhou, Felix T. S. Chan   +2 more
doaj   +1 more source

A Novel Network Security Risk Assessment Approach by Combining Subjective and Objective Weights under Uncertainty

Applied Sciences, 2018
Nowadays, computer networks are playing a more and more important role in people’s daily lives. Meanwhile, the security of computer networks has also attracted widespread concern.
Yancui Duan, Yonghua Cai, Zhikang Wang, Xinyang Deng   +3 more
doaj   +1 more source

On Measures of Uncertainty in Classification

IEEE Transactions on Signal Processing, 2023
Uncertainty is unavoidable in classification tasks and might originate from data (e.g., due to noise or wrong labeling), or the model (e.g., due to erroneous assumptions, etc). Providing an assessment of uncertainty associated with each outcome is of paramount importance in assessing the reliability of classification algorithms, especially on unseen ...
Saloua Chlaily, Debanshu Ratha, Pigi Lozou, Andrea Marinoni   +3 more
openaire   +3 more sources

Table of Contents

, 2022
Table of ...
The Measure
core   +1 more source

Mesurer et nommer la catastrophe : usages politiques des tremblements de terre du 6 février 2023 dans le sud-est de la Turquie

EchoGéo, 2023
The seismic disaster occurring in the southeastern part of Turkey was triggered by an earthquake of magnitude 7.8 at 01:17 (UTC) and a second of magnitude 7.5 at 10:24 (UTC) on Monday, February 6 2023.
Youenn Gourain, Solène Poyraz
doaj   +1 more source

An improved measure for belief structure in the evidence theory [PDF]

PeerJ Computer Science, 2021
Dempster–Shafer evidence theory (D–S theory) is suitable for processing uncertain information under complex circumstances. However, how to measure the uncertainty of basic probability distribution (BPA) in D–S theory is still an open question.
Qiang Zhang, Hao Li, Rongfei Li, Yongchuan Tang   +3 more
doaj   +2 more sources

Random Samples Generation from Neutrosophic Distributions Using the neutrostat Package in R [PDF]

Neutrosophic Sets and Systems
Random sample generation plays a fundamental role in statistical simulation, model validation, and performance evaluation. In the context of neutrosophic statistics, where uncertainty, indeterminacy, and imprecision are explicitly modeled, generating ...
M. I. Khan, Zahid Khan, Katrina Lane Krebs   +2 more
doaj   +1 more source

Modeling and Fusing the Uncertainty of FMEA Experts Using an Entropy-Like Measure with an Application in Fault Evaluation of Aircraft Turbine Rotor Blades

Entropy, 2018
As a typical tool of risk analysis in practical engineering, failure mode and effects analysis (FMEA) theory is a well known method for risk prediction and prevention.
Xuelian Zhou, Yongchuan Tang
doaj   +1 more source

Measurement uncertainty relations [PDF]

Journal of Mathematical Physics, 2014
Measurement uncertainty relations are quantitative bounds on the errors in an approximate joint measurement of two observables. They can be seen as a generalization of the error/disturbance tradeoff first discussed heuristically by Heisenberg. Here we prove such relations for the case of two canonically conjugate observables like position and momentum,
Busch, Paul, Lahti, Pekka, Werner, Reinhard F.   +2 more
openaire   +4 more sources

Using Explainable AI to Measure Feature Contribution to Uncertainty

Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022
The application of artificial intelligence techniques in safety-critical domains such as medicine and self-driving vehicles has raised questions regarding its trustworthiness and reliability.
Katherine Elizabeth Brown, Douglas A. Talbert   +1 more
doaj   +1 more source