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<Journal>
				<PublisherName></PublisherName>
				<JournalTitle>Transactions on Machine Intelligence</JournalTitle>
				<Issn>2821-1693</Issn>
				<Volume>1</Volume>
				<Issue>1</Issue>
				<PubDate PubStatus="epublish">
					<Year>2018</Year>
					<Month>03</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Intelligent Transportation in The Prevention of Accidents</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>49</FirstPage>
			<LastPage>56</LastPage>
			<ELocationID EIdType="pii">159820</ELocationID>
			
<ELocationID EIdType="doi">10.47176/TMI.2018.49</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>S.</FirstName>
					<LastName>Asadzadeh</LastName>
<Affiliation>Department of Computer Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran</Affiliation>

</Author>
<Author>
					<FirstName>O.</FirstName>
					<LastName>Rahmani Seryasat</LastName>
<Affiliation>Assistant Prof, Department of Electrical Engineering, Shams Higher Education Institute, Gorgan, Iran</Affiliation>
<Identifier Source="ORCID">0000-0002-9289-6128</Identifier>

</Author>
<Author>
					<FirstName>S.</FirstName>
					<LastName>Moshrefzadeh</LastName>
<Affiliation>Department of Computer Engineering, Faculty of Technology and Engineering, Yasouj University, Yasouj, Iran</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2022</Year>
					<Month>01</Month>
					<Day>16</Day>
				</PubDate>
			</History>
		<Abstract>Information technology has significantly influenced numerous industrial sectors, and its integration into transportation systems has emerged as a promising solution, giving rise to intelligent transportation systems (ITS). Among the key application areas of ITS is the use of computer vision for accident prevention. Specifically, intelligent driver monitoring systems play a crucial role in enhancing vehicle safety by proactively identifying and addressing conditions that may lead to accidents. These systems aim to assist and alert drivers by intelligently recognizing potentially dangerous situations, thereby contributing to a substantial reduction in traffic accidents and related incidents. A major concern within intelligent transportation is driver drowsiness, a critical factor in preventing severe financial and human losses caused by traffic accidents. To address this, intelligent systems are employed to enhance vehicle control by continuously analyzing the driver’s physical state and behavioral patterns. Consequently, the development of a system capable of accurately assessing a driver’s alertness or fatigue level based on both driver behavior and vehicle status holds great importance. Notably, the proposed system presented in this research demonstrates superior performance compared to existing approaches, achieving 96% accuracy, 94% sensitivity, and 94% specificity in detecting driver drowsiness.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Intelligent transportation</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">computer vision</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Accidents</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Smart city</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://www.tmachineintelligence.ir/article_159820_839da70edc02b98667e62acbd9da358d.pdf</ArchiveCopySource>
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