Abdulrahman AlAita, Muhammad Aslam, Khaled Al Sultan, Muhammad Saleem
{"title":"Analysis of Graeco-Latin square designs in the presence of uncertain data","authors":"Abdulrahman AlAita, Muhammad Aslam, Khaled Al Sultan, Muhammad Saleem","doi":"10.1186/s40537-024-00970-1","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Objective</h3><p>This paper addresses the Graeco-Latin square design (GLSD) under neutrosophic statistics. In this work, we propose a novel approach for analyzing Graeco-Latin square designs using uncertain observations.</p><h3 data-test=\"abstract-sub-heading\">Method</h3><p>This approach involves the determination of a neutrosophic ANOVA and the determination of the neutrosophic hypotheses and decision rule.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The performance of the proposed design is evaluated using the numerical examples and simulation study.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>Based on the results observed, it can be concluded that the GLSD under neutrosophic statistics performs better than the GLSD under classical statistics in the presence of uncertainty.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"2 1","pages":""},"PeriodicalIF":8.6000,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Big Data","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1186/s40537-024-00970-1","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Objective
This paper addresses the Graeco-Latin square design (GLSD) under neutrosophic statistics. In this work, we propose a novel approach for analyzing Graeco-Latin square designs using uncertain observations.
Method
This approach involves the determination of a neutrosophic ANOVA and the determination of the neutrosophic hypotheses and decision rule.
Results
The performance of the proposed design is evaluated using the numerical examples and simulation study.
Conclusion
Based on the results observed, it can be concluded that the GLSD under neutrosophic statistics performs better than the GLSD under classical statistics in the presence of uncertainty.
期刊介绍:
The Journal of Big Data publishes high-quality, scholarly research papers, methodologies, and case studies covering a broad spectrum of topics, from big data analytics to data-intensive computing and all applications of big data research. It addresses challenges facing big data today and in the future, including data capture and storage, search, sharing, analytics, technologies, visualization, architectures, data mining, machine learning, cloud computing, distributed systems, and scalable storage. The journal serves as a seminal source of innovative material for academic researchers and practitioners alike.