FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4360615211> ?p ?o ?g. }

• W4360615211 endingPage "779" @default.
• W4360615211 startingPage "779" @default.
• W4360615211 abstract "The most crucial component of any smart city traffic management system is traffic flow prediction. It can assist a driver in selecting the most efficient route to their destination. The digitalization of closed-circuit television (CCTV) systems has resulted in more effective and capable surveillance imaging systems for security applications. The number of automobiles on the world’s highways has steadily increased in recent decades. However, road capacity has not developed at the same rate, resulting in significantly increasing congestion. The model learning mechanism cannot be guided or improved by prior domain knowledge of real-world problems. In reality, symmetrical features are common in many real-world research objects. To mitigate this severe situation, the researchers chose adaptive traffic management to make intelligent and efficient use of the current infrastructure. Data grow exponentially and become a complex item that must be managed. Unstructured data are a subset of big data that are difficult to process and have volatile properties. CCTV cameras are used in traffic management to monitor a specific point on the roadway. CCTV generates unstructured data in the form of images and videos. Because of the data’s intricacy, these data are challenging to process. This study proposes using big data analytics to transform real-time unstructured data from CCTV into information that can be shown on a web dashboard. As a Hadoop-based architectural stack that can serve as the ICT backbone for managing unstructured data efficiently, the Hadoop Distributed File System (HDFS) stores several sorts of data using the Hadoop file storage system, a high-performance integrated virtual environment (HIVE) tables, and non-relational storage. Traditional computer vision algorithms are incapable of processing such massive amounts of visual data collected in real-time. However, the inferiority of traffic data and the quality of unit information are always symmetrical phenomena. As a result, there is a need for big data analytics with machine learning, which entails processing and analyzing vast amounts of visual data, such as photographs or videos, to uncover semantic patterns that may be interpreted. As a result, smart cities require a more accurate traffic flow prediction system. In comparison to other recent methods applied to the dataset, the proposed method achieved the highest accuracy of 98.21%. In this study, we look at the construction of a secure CCTV strategy that predicts traffic from CCTV surveillance using real-time traffic prediction analysis with generative adversarial networks (GAN) and HDFS." @default.
• W4360615211 created "2023-03-24" @default.
• W4360615211 creator A5064700135 @default.
• W4360615211 creator A5077809963 @default.
• W4360615211 date "2023-03-23" @default.
• W4360615211 modified "2023-09-27" @default.
• W4360615211 title "Generative Adversarial Networks (GAN) and HDFS-Based Realtime Traffic Forecasting System Using CCTV Surveillance" @default.
• W4360615211 cites W1610291649 @default.
• W4360615211 cites W2041569105 @default.
• W4360615211 cites W2348367549 @default.
• W4360615211 cites W2773104559 @default.
• W4360615211 cites W2788662981 @default.
• W4360615211 cites W2791520057 @default.
• W4360615211 cites W2802508687 @default.
• W4360615211 cites W2805089611 @default.
• W4360615211 cites W2885178111 @default.
• W4360615211 cites W2885453527 @default.
• W4360615211 cites W2893929991 @default.
• W4360615211 cites W2900579074 @default.
• W4360615211 cites W2964933785 @default.
• W4360615211 cites W2981573670 @default.
• W4360615211 cites W2989567700 @default.
• W4360615211 cites W2990842580 @default.
• W4360615211 cites W2994023724 @default.
• W4360615211 cites W3000301417 @default.
• W4360615211 cites W3003862857 @default.
• W4360615211 cites W3035235258 @default.
• W4360615211 cites W3036878027 @default.
• W4360615211 cites W3041279471 @default.
• W4360615211 cites W3044500917 @default.
• W4360615211 cites W3046210524 @default.
• W4360615211 cites W3046519666 @default.
• W4360615211 cites W3047411154 @default.
• W4360615211 cites W3048022923 @default.
• W4360615211 cites W3051293286 @default.
• W4360615211 cites W3084214008 @default.
• W4360615211 cites W3092477593 @default.
• W4360615211 cites W3092594866 @default.
• W4360615211 cites W3169552457 @default.
• W4360615211 cites W3210956393 @default.
• W4360615211 cites W4200008986 @default.
• W4360615211 cites W4206235370 @default.
• W4360615211 cites W4225391060 @default.
• W4360615211 cites W4285260582 @default.
• W4360615211 cites W4286587316 @default.
• W4360615211 cites W4307404564 @default.
• W4360615211 cites W4307937957 @default.
• W4360615211 cites W4308579925 @default.
• W4360615211 cites W4311393137 @default.
• W4360615211 cites W4319080181 @default.
• W4360615211 cites W2612120567 @default.
• W4360615211 doi "https://doi.org/10.3390/sym15040779" @default.
• W4360615211 hasPublicationYear "2023" @default.
• W4360615211 type Work @default.
• W4360615211 citedByCount "2" @default.
• W4360615211 countsByYear W43606152112023 @default.
• W4360615211 crossrefType "journal-article" @default.
• W4360615211 hasAuthorship W4360615211A5064700135 @default.
• W4360615211 hasAuthorship W4360615211A5077809963 @default.
• W4360615211 hasBestOaLocation W43606152111 @default.
• W4360615211 hasConcept C108583219 @default.
• W4360615211 hasConcept C111919701 @default.
• W4360615211 hasConcept C121332964 @default.
• W4360615211 hasConcept C124101348 @default.
• W4360615211 hasConcept C154945302 @default.
• W4360615211 hasConcept C168167062 @default.
• W4360615211 hasConcept C2777103469 @default.
• W4360615211 hasConcept C2781252014 @default.
• W4360615211 hasConcept C33499554 @default.
• W4360615211 hasConcept C38652104 @default.
• W4360615211 hasConcept C41008148 @default.
• W4360615211 hasConcept C75684735 @default.
• W4360615211 hasConcept C77088390 @default.
• W4360615211 hasConcept C79403827 @default.
• W4360615211 hasConcept C81860439 @default.
• W4360615211 hasConcept C97355855 @default.
• W4360615211 hasConcept C98045186 @default.
• W4360615211 hasConceptScore W4360615211C108583219 @default.
• W4360615211 hasConceptScore W4360615211C111919701 @default.
• W4360615211 hasConceptScore W4360615211C121332964 @default.
• W4360615211 hasConceptScore W4360615211C124101348 @default.
• W4360615211 hasConceptScore W4360615211C154945302 @default.
• W4360615211 hasConceptScore W4360615211C168167062 @default.
• W4360615211 hasConceptScore W4360615211C2777103469 @default.
• W4360615211 hasConceptScore W4360615211C2781252014 @default.
• W4360615211 hasConceptScore W4360615211C33499554 @default.
• W4360615211 hasConceptScore W4360615211C38652104 @default.
• W4360615211 hasConceptScore W4360615211C41008148 @default.
• W4360615211 hasConceptScore W4360615211C75684735 @default.
• W4360615211 hasConceptScore W4360615211C77088390 @default.
• W4360615211 hasConceptScore W4360615211C79403827 @default.
• W4360615211 hasConceptScore W4360615211C81860439 @default.
• W4360615211 hasConceptScore W4360615211C97355855 @default.
• W4360615211 hasConceptScore W4360615211C98045186 @default.
• W4360615211 hasIssue "4" @default.
• W4360615211 hasLocation W43606152111 @default.
• W4360615211 hasOpenAccess W4360615211 @default.
• W4360615211 hasPrimaryLocation W43606152111 @default.

• next