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A new stock price forecasting method using active deep learning approach

AlKhatib, Khalid; Khazaleh, Huthaifa; Alkhazaleh, Hamzah Ali - In: Journal of Open Innovation: Technology, Market, and … 8 (2022) 2, pp. 1-23

the loss result is also considered. Finally, the study included six deep learning models, MLP, GRU, LSTM, Bi-LSTM, CNN …, and CNN-LSTM, to predict the adjusted closing price of the stocks. The six variables used (High, Low, Open, Volume, HiLo …

Persistent link: https://www.econbiz.de/10014329596

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/10014494935

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/10013326270

Fault diagnosis of an industrial gas turbine based on the thermodynamic model coupled with a multi feedforward artificial neural networks

Alblawi, Adel - In: Energy Reports 6 (2020), pp. 1083-1096

In the study presented in this paper, the deterioration in the performance of an industrial gas turbine during the operation design point was simulated by using the thermodynamic principle and a multi feedforward artificial neural networks (MFANN) system. Initially the thermodynamic model was...

Persistent link: https://www.econbiz.de/10012652465