Developing Novel Algorithms for Generating Inexact Data Through Triangle Distribution

The manuscript introduces the mathematical representation of the neutrosophic triangular distribution, encompassing probability density functions and cumulative distribution functions. Two algorithms are introduced for the generation of random variates based on this distribution. Through simulations and a comparative examination with traditional statistical approaches, the research illustrates the versatility and resilience of the neutrosophic triangular distribution. The findings underscore its effectiveness in addressing uncertainty, particularly in scenarios with varying degrees of indeterminacy. The study emphasizes the substantial influence of uncertainty on the generation of random variates, with potential implications for decision-making and data analysis.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Big Data
Published
2024-09-13
DOI
https://doi.org/10.1109/tbdata.2024.3460529
Primary Topic
Mathematics and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Developing Novel Algorithms for Generating Inexact Data Through Triangle Distribution

Muhammad Aslam
IEEE Transactions on Big Data
Mathematics and Applications
article

Developing Novel Algorithms for Generating Inexact Data Through Triangle Distribution

Muhammad Aslam
article en

Abstract

The manuscript introduces the mathematical representation of the neutrosophic triangular distribution, encompassing probability density functions and cumulative distribution functions. Two algorithms are introduced for the generation of random variates based on this distribution. Through simulations and a comparative examination with traditional statistical approaches, the research illustrates the versatility and resilience of the neutrosophic triangular distribution. The findings underscore its effectiveness in addressing uncertainty, particularly in scenarios with varying degrees of indeterminacy. The study emphasizes the substantial influence of uncertainty on the generation of random variates, with potential implications for decision-making and data analysis.

IEEE Transactions on Big DataVol. 11(3)
King Abdulaziz University (SA)
Openalex Percentile: Top 75%
Mathematics and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Developing Novel Algorithms for Generating Inexact Data Through Triangle Distribution — Muhammad Aslam · IEEE Transactions on Big Data (2024) | TGRS Research Map | TGRS