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Edge AI-Based Automated Detection and Classification of Road Anomalies in VANET Using Deep Learning.
Bibi, Rozi; Saeed, Yousaf; Zeb, Asim; Ghazal, Taher M; Rahman, Taj; Said, Raed A; Abbas, Sagheer; Ahmad, Munir; Khan, Muhammad Adnan.
Afiliación
  • Bibi R; Department of Information Technology, The University of Haripur, Haripur, Pakistan.
  • Saeed Y; Department of Information Technology, The University of Haripur, Haripur, Pakistan.
  • Zeb A; Department of Computer Science, Abbottabad University of Science and Technology, Havelian, Pakistan.
  • Ghazal TM; Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600 Bangi, Selangor, Malaysia.
  • Rahman T; School of Information Technology, Skyline University College, University City Sharjah, 1797 Sharjah, UAE.
  • Said RA; Department of Physical & Numerical Science, Qurtuba University of Science & Information Technology, Peshawar 25000, Pakistan.
  • Abbas S; Canadian University Dubai, Dubai, UAE.
  • Ahmad M; School of Computer Science, National College of Business Administration and Economics, Lahore 54000, Pakistan.
  • Khan MA; School of Computer Science, National College of Business Administration and Economics, Lahore 54000, Pakistan.
Comput Intell Neurosci ; 2021: 6262194, 2021.
Article en En | MEDLINE | ID: mdl-34630550
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Accidentes de Tránsito / Aprendizaje Profundo Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: Comput Intell Neurosci Asunto de la revista: INFORMATICA MEDICA / NEUROLOGIA Año: 2021 Tipo del documento: Article País de afiliación: Pakistán Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Accidentes de Tránsito / Aprendizaje Profundo Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: Comput Intell Neurosci Asunto de la revista: INFORMATICA MEDICA / NEUROLOGIA Año: 2021 Tipo del documento: Article País de afiliación: Pakistán Pais de publicación: Estados Unidos