Hossein Ghalavand, Mahboobeh Momtazan, Saeed Jelvay, Seyed Hojjat Khatamifar, Zahra Heydarvand
Iranian medical students' E-learning continuance intention after end of COVID-19 pandemic
Journal of education and health promotion
deep neural networks dnns complex structure multiple nonlinear processing units achieved great successes feature learning image visualization analysis due interpretability black box problem dnns however still many obstacles applications dnns various realworld cases paper proposes new dnn model knowledgebased deep stacked denoising autoencoders kbsdae inserts knowledge ie confidence classification rules deep network structure model offer good understanding representations learned deep network also produce improvement learning performance stacked denoising autoencoder sdae knowledge discovery algorithm proposed extract confidence rules interpret layerwise network ie denoising autoencoder dae symbolic language developed describe deep network shows suitable representation quantitative reasoning deep network confidence rule insertion deep network able produce improvement feature learning daes classification rules extracted data offer novel method knowledge insertion classification layer sdae testing results kbsdae various benchmark data indicate proposed method effectively extracts knowledge deep network also shows better feature learning performance typical dnns eg sdae