Attention Autoencoder for Generative Latent Representational Learning in Anomaly Detection.

Oluwasanmi, Ariyo; Aftab, Muhammad Umar; Baagyere, Edward; Qin, Zhiguang; Ahmad, Muhammad; Mazzara, Manuel

Oluwasanmi, Ariyo; Aftab, Muhammad Umar; Baagyere, Edward; Qin, Zhiguang; Ahmad, Muhammad; Mazzara, Manuel.

Afiliación

Oluwasanmi A; School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
Aftab MU; Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan.
Baagyere E; School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
Qin Z; School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
Ahmad M; Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan.
Mazzara M; Institute of Software Development and Engineering, Innopolis University, Innopolis 420500, Russia.

Sensors (Basel) ; 22(1)2021 Dec 24.

Article en En | MEDLINE | ID: mdl-35009666

Asunto(s)

Algoritmos; Inteligencia Artificial; Atención; Electrocardiografía; Humanos; Distribución Normal

Palabras clave

anomaly detection; attention module; autoencoder; long short-term memory (LSTM); variational autoencoder (VAE)

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Algoritmos / Inteligencia Artificial Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Sensors (Basel) Año: 2021 Tipo del documento: Article País de afiliación: China

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