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	<title>hyperbolic fuzzy sets &#8211; BIOENGINEER.ORG</title>
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		<title>Simple Hyperbolic Fuzzy Sets Outperform Complex Diophantine Models in Accuracy Test</title>
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		<pubDate>Tue, 06 Oct 2026 11:24:47 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[classification accuracy]]></category>
		<category><![CDATA[computational complexity]]></category>
		<category><![CDATA[decision stability]]></category>
		<category><![CDATA[Diophantine fuzzy sets]]></category>
		<category><![CDATA[effect size]]></category>
		<category><![CDATA[fuzzy sets]]></category>
		<category><![CDATA[hyperbolic fuzzy sets]]></category>
		<category><![CDATA[multi-criteria decision-making]]></category>
		<category><![CDATA[parameter sensitivity]]></category>
		<category><![CDATA[scalability]]></category>
		<category><![CDATA[Sensitivity analysis]]></category>
		<category><![CDATA[uncertainty modeling]]></category>
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					<description><![CDATA[A new study finds that parameter-free hyperbolic fuzzy sets achieve perfect classification accuracy while heavily parameterized Diophantine fuzzy models collapse to as low as 43 percent, exposing a fundamental trade-off between flexibility and decision reliability.]]></description>
		
		
		
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