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	<title>transport safety performance analysis &#8211; BIOENGINEER.ORG</title>
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		<title>Revolutionary Hybrid Model Boosts Transport Safety Engineering</title>
		<link>https://bioengineer.org/revolutionary-hybrid-model-boosts-transport-safety-engineering/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Mon, 20 Oct 2025 09:15:07 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[decision-making in transport safety]]></category>
		<category><![CDATA[graph theory applications in transport]]></category>
		<category><![CDATA[hybrid MCDM-machine learning models]]></category>
		<category><![CDATA[transport safety engineering innovations]]></category>
		<category><![CDATA[transport safety performance analysis]]></category>
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					<description><![CDATA[In today’s rapidly evolving world, the intersection of advanced computational methods and decision-making is becoming increasingly critical, particularly in areas as vital as transport safety engineering. A groundbreaking study has introduced a sophisticated hybrid multiple criteria decision-making (MCDM) model that integrates machine learning techniques. This innovative DCRITIC-WASPAS-K-means model is further enhanced by a graph-based approach [&#8230;]]]></description>
		
		
		
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