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		<title>Fuzzy Rules and Evolutionary Algorithms Offer an Auditable Way to Fuse Wearable Health Alerts</title>
		<link>https://bioengineer.org/fuzzy-rules-and-evolutionary-algorithms-offer-an-auditable-way-to-fuse-wearable-health-alerts/</link>
		
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		<pubDate>Thu, 15 Oct 2026 05:56:19 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[ANFIS]]></category>
		<category><![CDATA[elder-health monitoring]]></category>
		<category><![CDATA[fuzzy inference system]]></category>
		<category><![CDATA[interpretable artificial intelligence]]></category>
		<category><![CDATA[missing-modality handling]]></category>
		<category><![CDATA[monitoring-priority index]]></category>
		<category><![CDATA[NSGA-II]]></category>
		<category><![CDATA[Pareto optimization]]></category>
		<category><![CDATA[Reproducibility]]></category>
		<category><![CDATA[risk fusion]]></category>
		<category><![CDATA[synthetic benchmark]]></category>
		<category><![CDATA[wearable sensing]]></category>
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					<description><![CDATA[A new Scientific Reports study presents an interpretable ANFIS-NSGA-II framework that fuses seven wearable health risk modules into an auditable monitoring-priority index, showing on synthetic data that transparency need not cost predictive performance.]]></description>
		
		
		
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