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Predicting poverty with vegetation index

Tang, Binh; Liu, Yanyan; Matteson, David S. - In: Applied economic perspectives and policy 44 (2022) $m6, pp. 930-945

Persistent link: https://www.econbiz.de/10013274058

Convolutional neural network forecasting of European Union allowances futures using a novel unconstrained transformation method

Huang, Wenyang; Wang, Huiwen; Qin, Haotong; Wei, Yigang; … - In: Energy economics 110 (2022), pp. 1-16

Persistent link: https://www.econbiz.de/10013349896

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/10013266369

Carbon trading price forecasting based on parameter optimization VMD and deep network CNN-LSTM model

Ling, Meijun; Cao, Guangxi - In: International journal of financial engineering 11 (2024) 1, pp. 1-39

Persistent link: https://www.econbiz.de/10014521304

Optimised hybrid CNN-LSTM model for stock price prediction

Patnaik, Deepti; Rao, N. V. Jagannadha; Padhiari, … - In: International journal of management and decision making … 23 (2024) 4, pp. 438-460

Persistent link: https://www.econbiz.de/10015067129

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

A comparative study of statistical machine learning methods for condition monitoring of electric drive trains in supply chains

Lahmiri, Salim - In: Supply chain analytics 2 (2023), pp. 1-7

Fault detection and identification are critical for the accurate maintenance and management of industrial machinery. In this regard, data-driven condition monitoring models play an important role in machinery fault diagnosis and management. This study investigates the applicability of various...

Persistent link: https://www.econbiz.de/10014516481