The Effect of Unknown and Uncertain Information on the Entropy for Vague Set

2008 ◽  
Author(s):  
Gu Mei ◽  
Yan Hong Li
Keyword(s):  
Uncertain Information ◽  
Vague Set

Research on Vague Set of Target Threat Information Fusion Algorithm

2014 ◽  
Vol 599-601 ◽  
pp. 1671-1678
Author(s):  
Ke Hu Xu ◽  
Jin Yu Chen ◽  
De Peng Kong ◽  
Pu Fan
Keyword(s):  
Information Fusion ◽  
Scoring Function ◽  
Score Function ◽  
Quantitative Information ◽  
Uncertain Information ◽  
Fusion Technology ◽  
Fusion Algorithm ◽  
Information Process ◽  
Vague Set ◽  
Research Areas

Information fusion technology is one of the most active research areas currently, which is affected by the fact that the computer can only handle quantitative information and the result cannot reflect the actual feature of the target sometimes. This paper makes use of advantages of the vague set in dealing with uncertain information process to establish the target threat sequencing model based on vague set and take both quantitative and qualitative information of target into account. Using the improved scoring function, this paper comes up with the target threat sequence steps based on extreme score function method to provide a better data supporting for the decision-making function of information fusion technology.

scholarly journals A New Approach of Functional Dependency in a Neutrosophic Relational Database Model

2019 ◽  
Vol 8 (2) ◽  
pp. 44-48
Author(s):  
Soumitra De ◽  
Jaydev Mishra
Keyword(s):  
Relational Database ◽  
Classical Case ◽  
Functional Dependency ◽  
Neutrosophic Set ◽  
Uncertain Information ◽  
Neutrosophic Sets ◽  
New Approach ◽  
Vague Set ◽  
Database Model ◽  
Relational Data Model

In order to model the imprecise and uncertain information, different classical relational data model have been studied in literature using vague set theory. However, neutrosophic set, as a generalized vague set, has more powerful ability to process fuzzy information than vague set. In this paper, we have proposed a neutrosophic relational database model and have defined a new kind of neutrosophic functional dependency (called  -nfd) based on the -equality of tuples and the similarity measure of neutrosophic sets. Next, we present a set of sound neutrosophic inference rules which are similar to Armstrong’s axioms for the classical case. Finally, partial  -nfdand neutrosophic key have been studied with the new notion of  -nfdand also tested.

The choice of optimum cross section for overhead line by economic intervals' method

2011 ◽  
Vol 28 (1) ◽  
pp. 37-42 ◽  
Author(s):  
Svetlana Guseva ◽  
Lubov Petrichenko
Keyword(s):  
Cross Section ◽  
Current Density ◽  
Uncertain Information ◽  
Overhead Line ◽  
Market Conditions ◽  
Overhead Lines ◽  
Mathcad Software

The choice of optimum cross section for overhead line by economic intervals' methodIn this paper an approach to choosing the optimum cross section for overhead line in conditions of incomplete and uncertain information is considered. The two methods of such choice are presented: method of economic current density and economic intervals' method. The correction of the economic intervals method is offered under market conditions of costs. As example 20 kV and 110 kV overhead lines with aluminum, copper and ferroaluminum wires are selected. Universal nomograms with different standard cross section are calculated and constructed. The graphics using Mathcad software are offered.

Decision Support Under Incomplete and Uncertain Information

1998 ◽  
Vol 31 (20) ◽  
pp. 721-726
Author(s):  
Arkady Borisov ◽  
Clara Savchenko
Keyword(s):  
Decision Support ◽  
Uncertain Information

Processing uncertain information in the linear space of fuzzy sets

1991 ◽  
Vol 44 (2) ◽  
pp. 187-198 ◽  
Author(s):  
Wladyslaw Homenda ◽  
Witold Pedrycz
Keyword(s):  
Fuzzy Sets ◽  
Linear Space ◽  
Uncertain Information

Extended two-dimensional belief function based on divergence measurement

2020 ◽  
pp. 1-8
Author(s):  
Jianping Fan ◽  
Jing Wang ◽  
Meiqin Wu
Keyword(s):  
Belief Function ◽  
Distribution Functions ◽  
Divergence Measure ◽  
Uncertain Information ◽  
Belief Functions ◽  
Two Dimensional ◽  
Probability Distribution Functions ◽  
Basic Probability ◽  
The Difference ◽  
Supporting Degree

The two-dimensional belief function (TDBF = (mA, mB)) uses a pair of ordered basic probability distribution functions to describe and process uncertain information. Among them, mB includes support degree, non-support degree and reliability unmeasured degree of mA. So it is more abundant and reasonable than the traditional discount coefficient and expresses the evaluation value of experts. However, only considering that the expert’s assessment is single and one-sided, we also need to consider the influence between the belief function itself. The difference in belief function can measure the difference between two belief functions, based on which the supporting degree, non-supporting degree and unmeasured degree of reliability of the evidence are calculated. Based on the divergence measure of belief function, this paper proposes an extended two-dimensional belief function, which can solve some evidence conflict problems and is more objective and better solve a class of problems that TDBF cannot handle. Finally, numerical examples illustrate its effectiveness and rationality.

scholarly journals Neutrosophic ratio-type estimators for estimating the population mean

2021 ◽  
Author(s):  
Zaigham Tahir ◽  
Hina Khan ◽  
Muhammad Aslam ◽  
Javid Shabbir ◽  
Yasar Mahmood ◽  
...  
Keyword(s):  
Minimum Mean Square Error ◽  
Auxiliary Information ◽  
Population Parameter ◽  
Uncertain Information ◽  
Population Mean ◽  
Classical Statistics ◽  
Neutrosophic Statistics ◽  
The Mean ◽  
Type Data ◽  
First Time

AbstractAll researches, under classical statistics, are based on determinate, crisp data to estimate the mean of the population when auxiliary information is available. Such estimates often are biased. The goal is to find the best estimates for the unknown value of the population mean with minimum mean square error (MSE). The neutrosophic statistics, generalization of classical statistics tackles vague, indeterminate, uncertain information. Thus, for the first time under neutrosophic statistics, to overcome the issues of estimation of the population mean of neutrosophic data, we have developed the neutrosophic ratio-type estimators for estimating the mean of the finite population utilizing auxiliary information. The neutrosophic observation is of the form $${Z}_{N}={Z}_{L}+{Z}_{U}{I}_{N}\, {\rm where}\, {I}_{N}\in \left[{I}_{L}, {I}_{U}\right], {Z}_{N}\in [{Z}_{l}, {Z}_{u}]$$ Z N = Z L + Z U I N where I N ∈ I L , I U , Z N ∈ [ Z l , Z u ] . The proposed estimators are very helpful to compute results when dealing with ambiguous, vague, and neutrosophic-type data. The results of these estimators are not single-valued but provide an interval form in which our population parameter may have more chance to lie. It increases the efficiency of the estimators, since we have an estimated interval that contains the unknown value of the population mean provided a minimum MSE. The efficiency of the proposed neutrosophic ratio-type estimators is also discussed using neutrosophic data of temperature and also by using simulation. A comparison is also conducted to illustrate the usefulness of Neutrosophic Ratio-type estimators over the classical estimators.

Reason-maintenance Belief Logic with Uncertain Information

2020 ◽  
Vol 21 (1) ◽  
pp. 1-32
Author(s):  
Tuan-Fang Fan ◽  
Churn-Jung Liau
Keyword(s):  
Uncertain Information ◽  
Reason Maintenance ◽  
Belief Logic

scholarly journals Pediatric Oncologists’ Experiences Returning and Incorporating Genomic Sequencing Results into Cancer Care

2021 ◽  
Vol 11 (6) ◽  
pp. 570
Author(s):  
Rebecca L. Hsu ◽  
Amanda M. Gutierrez ◽  
Sophie K. Schellhammer ◽  
Jill O. Robinson ◽  
Sarah Scollon ◽  
...  
Keyword(s):  
Cancer Care ◽  
Cancer Genomics ◽  
Emotional Responses ◽  
Genetic Counselors ◽  
Genomic Sequencing ◽  
Uncertain Information ◽  
Time Points ◽  
Genomic Results

Pediatric oncologists’ perspectives around returning and incorporating tumor and germline genomic sequencing (GS) results into cancer care are not well-described. To inform optimization of cancer genomics communication, we assessed oncologists’ experiences with return of genomic results (ROR), including their preparation/readiness for ROR, collaboration with genetic counselors (GCs) during ROR, and perceived challenges. The BASIC3 study paired pediatric oncologists with GCs to return results to patients’ families. We thematically analyzed 24 interviews with 12 oncologists at two post-ROR time points. Oncologists found pre-ROR meetings with GCs and geneticists essential to interpreting patients’ reports and communicating results to families. Most oncologists took a collaborative ROR approach where they discussed tumor findings and GCs discussed germline findings. Oncologists perceived many roles for GCs during ROR, including answering families’ questions and describing information in lay language. Challenges identified included conveying uncertain information in accessible language, limits of oncologists’ genetics expertise, and navigating families’ emotional responses. Oncologists emphasized how GCs’ and geneticists’ support was essential to ROR, especially for germline findings. GS can be successfully integrated into cancer care, but to account for the GC shortage, alternative ROR models and access to genetics resources will be needed to better support families and avoid burdening oncologists.

Sign in / Sign up

Export Citation Format

Share Document