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The Synergy of Simplicity and Vagueness: Exploring Simple Statistics in Fuzzy Mathematical Frameworks |
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PP: 457-465 |
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doi:10.18576/amis/190219
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Author(s) |
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Suleiman Ibrahim Mohammad,
N. Yogeesh,
N. Raja,
R. Chetana,
P. William,
Asokan Vasudevan,
Badrea AlQraini,
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Abstract |
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| Researchers have studied the combination of statistical methods and fuzzy logic as a hybrid when resolving problems that are specific and less random. Statistical methods, based on quantitative analysis, offer powerful instruments like mean, variance, and correlation to assess numeric data. Fuzzy logic, in contrast, uses linguistic variables, membership functions, and fuzzy rules to represent uncertainty and ambiguity, and is thus well-suited to qualitative information. These techniques are integrated into hybrid statistical- fuzzy frameworks that provide holistic solutions to several fields. To illustrate what this integration would look like, we performed a case study with real data that contained both quantitative scores and qualitative risk levels. The mean (57.50), variance (466.25), and standard deviation (21.59) were some of the statistical measures that we computed for understanding centrality and dispersion of the data. Defuzzification through fuzzy logic complemented the analytical processes by producing a nuanced risk score (64.34) reflecting qualitative components. Bar charts and scatter plots demonstrated their complimentary nature. Hybrid models are an effective way to integrate statistical accuracy and fuzzy flexibility that can be implemented in risk assessment, decision-making, and multi-objective optimization. Thus, the use of these frameworks will continue to evolve with future opportunities including machine learning, adaptive membership functions and increased mathematical efficiency furthering the utilization of these frameworks. The combination of AI and True Artificial Intelligence can facilitate breakthroughs in their applications in fields like healthcare, finance, smart systems, and others.
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