FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4384068701> ?p ?o ?g. }

• W4384068701 abstract "INTRODUCTION: Although scientific hypothesis testing methodologies are well established, their application to falsifiable hypothesis testing for assessing causal relationships potentially identified by machine learning and artificial intelligence models is rare due to the primarily nonparametric statistical nature of these systems.OBJECTIVES: The primary objective of this study is to demonstrate the potential for applying nonparametric statistical tests to a mixed qualitative and quantitative cyber network dataset as a method to pre-assess the feasibility of applying forms of statistical hypothesis testing before a machine learning algorithm models the data.METHODS: A mixture of permuted analysis of variance models augmented by the use of transformed non-Euclidean multivariate distances between curated dependent variable classes produced this research data. Quasi-experimental data from an enclosed laboratory environment utilizing a monitored, locally unrestricted network that introduced known Internet of Things (IoT) malware software supplied network flow events.RESULTS: A PERMANOVA model was executed against 62,000 records of the network flow observations, using Euclidean distance measurements with variable-dependent relationship ordering, using terms added sequentially (first to last) in the order encountered in the raw network flow dataset, using 200 permutations. This precursor test resulted in a p-value for the PERMANOVA model that incorporated terms added sequentially of 0.02985, providing an F value of 0.00017 with which to determine the ratio of explained to unexplained variance. Utilizing an analysis of the F values for all of the residuals, we show 29,998 degrees of freedom with a residual F model score of 0.99983, indicating that there is a strong proportion of explained to unexplained variance across all of the independent variables contained in the model. The model is thus statistically significant with a p-value below the alpha test statistic of 0.05.CONCLUSION: This research has demonstrated that it is possible to apply tests of falsifiability that incorporate reproducible methods into the quasi-experiment design and apply this to the field of machine learning. Applied to AI/ML (artificial intelligence/machine learning) models, this pre-assessment methodology supports the appropriateness of cyber network flow datasets in which a final test of statistical significance would be required. The authors believe that this represents a substantially useful precursor assessment stage for the suitability and reliability of the utilization of any nonparametric statistical learning algorithms applied to cyber network data predictive analytics." @default.
• W4384068701 created "2023-07-13" @default.
• W4384068701 creator A5024919511 @default.
• W4384068701 creator A5074610060 @default.
• W4384068701 date "2023-07-12" @default.
• W4384068701 modified "2023-09-28" @default.
• W4384068701 title "A Framework for Utilizing Permutational Multiple Analysis of Variance as a Precursor for Nonparametric Statistical Learning with Cyber Network Data" @default.
• W4384068701 cites W2149421543 @default.
• W4384068701 cites W2737411906 @default.
• W4384068701 cites W2800003326 @default.
• W4384068701 cites W2955014922 @default.
• W4384068701 doi "https://doi.org/10.4108/eetcasa.v9i1.2929" @default.
• W4384068701 hasPublicationYear "2023" @default.
• W4384068701 type Work @default.
• W4384068701 citedByCount "0" @default.
• W4384068701 crossrefType "journal-article" @default.
• W4384068701 hasAuthorship W4384068701A5024919511 @default.
• W4384068701 hasAuthorship W4384068701A5074610060 @default.
• W4384068701 hasBestOaLocation W43840687011 @default.
• W4384068701 hasConcept C102366305 @default.
• W4384068701 hasConcept C105795698 @default.
• W4384068701 hasConcept C114289077 @default.
• W4384068701 hasConcept C117251300 @default.
• W4384068701 hasConcept C119857082 @default.
• W4384068701 hasConcept C121955636 @default.
• W4384068701 hasConcept C124101348 @default.
• W4384068701 hasConcept C134306372 @default.
• W4384068701 hasConcept C144133560 @default.
• W4384068701 hasConcept C154945302 @default.
• W4384068701 hasConcept C182365436 @default.
• W4384068701 hasConcept C196083921 @default.
• W4384068701 hasConcept C33923547 @default.
• W4384068701 hasConcept C41008148 @default.
• W4384068701 hasConcept C87007009 @default.
• W4384068701 hasConceptScore W4384068701C102366305 @default.
• W4384068701 hasConceptScore W4384068701C105795698 @default.
• W4384068701 hasConceptScore W4384068701C114289077 @default.
• W4384068701 hasConceptScore W4384068701C117251300 @default.
• W4384068701 hasConceptScore W4384068701C119857082 @default.
• W4384068701 hasConceptScore W4384068701C121955636 @default.
• W4384068701 hasConceptScore W4384068701C124101348 @default.
• W4384068701 hasConceptScore W4384068701C134306372 @default.
• W4384068701 hasConceptScore W4384068701C144133560 @default.
• W4384068701 hasConceptScore W4384068701C154945302 @default.
• W4384068701 hasConceptScore W4384068701C182365436 @default.
• W4384068701 hasConceptScore W4384068701C196083921 @default.
• W4384068701 hasConceptScore W4384068701C33923547 @default.
• W4384068701 hasConceptScore W4384068701C41008148 @default.
• W4384068701 hasConceptScore W4384068701C87007009 @default.
• W4384068701 hasIssue "1" @default.
• W4384068701 hasLocation W43840687011 @default.
• W4384068701 hasOpenAccess W4384068701 @default.
• W4384068701 hasPrimaryLocation W43840687011 @default.
• W4384068701 hasRelatedWork W1968663748 @default.
• W4384068701 hasRelatedWork W1970708522 @default.
• W4384068701 hasRelatedWork W1987934484 @default.
• W4384068701 hasRelatedWork W2011903341 @default.
• W4384068701 hasRelatedWork W2163167269 @default.
• W4384068701 hasRelatedWork W2169910187 @default.
• W4384068701 hasRelatedWork W2262581580 @default.
• W4384068701 hasRelatedWork W2794649148 @default.
• W4384068701 hasRelatedWork W2998750086 @default.
• W4384068701 hasRelatedWork W4366404590 @default.
• W4384068701 hasVolume "9" @default.
• W4384068701 isParatext "false" @default.
• W4384068701 isRetracted "false" @default.
• W4384068701 workType "article" @default.