Semantic Reasoning with Uncertain Information from Unreliable Sources

2016 ◽  
pp. 92-109
Author(s):  
Murat Şensoy ◽  
Lance Kaplan ◽  
Geeth de Mel
Keyword(s):  
Uncertain Information ◽  
Semantic Reasoning

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.

Semantic Reasoning on Mobile Devices: Do Androids Dream of Efficient Reasoners?

2015 ◽  
Author(s):  
Carlos Bobed ◽  
Roberto Yus ◽  
Fernando Bobillo ◽  
Eduardo Mena
Keyword(s):  
Mobile Devices ◽  
Semantic Reasoning

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.

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