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A deep learning model framework for diabetic retinopathy detection

Padmapriya, M.; Pasupathy, S.; Sumathi, R.; Punitha, V. - In: International journal of networking and virtual … 27 (2022) 2, pp. 107-124

Persistent link: https://www.econbiz.de/10014314585

A comparative review of sentimental analysis using machine learning and deep learning approaches

Nagelli, Archana; Saleena, B. - In: Journal of information & knowledge management : JIKM 22 (2023) 3, pp. 1-32

Persistent link: https://www.econbiz.de/10014388595

Historical calibration of SVJD models with deep learning

Fičura, Milan; Witzany, Jiří - 2023

-Diffusion (SVJD) models to historical asset return time series. 1-Dimensional Convolutional Neural Networks (1D-CNN) are used for that …

Persistent link: https://www.econbiz.de/10014444774

Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors : state of the art survey

Drakaki, Maria; Karnavas, Yannis L.; Tziafettas, Ioannis A. - In: Journal of industrial engineering and management : JIEM 15 (2022) 1, pp. 31-57

Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D...

Persistent link: https://www.econbiz.de/10012815091

Fault detection and recognition of multivariate process based on feature learning of one-dimensional convolutional neural network and stacked denoised autoencoder

Zhang, Chengyi; Yu, Jianbo; Wang, Shijin - In: International journal of production research 59 (2021) 8, pp. 2426-2449

Persistent link: https://www.econbiz.de/10012501291