Uncertainty-driven generation of neutrosophic random variates from the Weibull distribution

Objective This paper aims to introduce an algorithm designed for generating random variates in situations characterized by uncertainty . Method The paper outlines the development of two distinct algorithms for producing both minimum and maximum neutrosophic data based on the Weibull distribution. Re...

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Bibliographic Details
Published in:Journal of big data 2023-12, Vol.10 (1), p.177-17, Article 177
Main Author: Aslam, Muhammad
Format: Article
Language:English
Subjects:
Algorithms
Big Data
Classical statistics
Communications Engineering
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Database Management
Indeterminacy
Information Storage and Retrieval
Mathematical Applications in Computer Science
Networks
Neutrosophic data
Random variables
Simulation
Uncertainty
Weibull distribution
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Summary:Objective This paper aims to introduce an algorithm designed for generating random variates in situations characterized by uncertainty . Method The paper outlines the development of two distinct algorithms for producing both minimum and maximum neutrosophic data based on the Weibull distribution. Results Through comprehensive simulations, the efficacy of these algorithms has been thoroughly assessed. The paper includes tables presenting neutrosophic random data and an in-depth analysis of how uncertainty impacts these values. Conclusion The study's findings demonstrate a noteworthy correlation between the degree of uncertainty and the neutrosophic minimum and maximum data. As uncertainty intensifies, these values exhibit a tendency to decrease.
ISSN:2196-1115
2196-1115
DOI:10.1186/s40537-023-00860-y