An AI-Driven Hybrid Framework for Intrusion Detection in IoT-Enabled E-Health.

Wahab, Fazal; Zhao, Yuhai; Javeed, Danish; Al-Adhaileh, Mosleh Hmoud; Almaaytah, Shahab Ahmad; Khan, Wasiat; Saeed, Muhammad Shahid; Kumar Shah, Rajeev

Wahab, Fazal; Zhao, Yuhai; Javeed, Danish; Al-Adhaileh, Mosleh Hmoud; Almaaytah, Shahab Ahmad; Khan, Wasiat; Saeed, Muhammad Shahid; Kumar Shah, Rajeev.

Afiliação

Wahab F; College of Computer Science and Technology, Northeastern University, Shenyang 110169, China.
Zhao Y; College of Computer Science and Technology, Northeastern University, Shenyang 110169, China.
Javeed D; Software College, Northeastern University, Shenyang 110169, China.
Al-Adhaileh MH; Deanship of E-Learning and Distance Education, King Faisal University, P.O. Box 400, Al-Ahsa, Saudi Arabia.
Almaaytah SA; Applied College in Abqaq, King Faisal University, Al-Ahsa, Saudi Arabia.
Khan W; Department of Software Engineering, University of Science and Technology Bannu, Bannu, Pakistan.
Saeed MS; Dalian University of Technology, Dalian 116024, China.
Kumar Shah R; Sunway International Business School, Kathmandu, Nepal.

Comput Intell Neurosci ; 2022: 6096289, 2022.

Article em En | MEDLINE | ID: mdl-36045979

Assuntos

Inteligência Artificial; Telemedicina; Big Data

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Telemedicina Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Comput Intell Neurosci Assunto da revista: INFORMATICA MEDICA / NEUROLOGIA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China

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