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Yufeng Huang, Jun Tao, Gang Sun, Hao Zhang and Yan Hu
In this study, a prognostics and health management (PHM) framework is proposed for aero-engines, which combines a dynamic probability (DP) model and a long short-term memory neural network (LSTM). A DP model based on Gaussian mixture model-adaptive densi...
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Maciej Trzcinski, Piotr A. Kowalski and Szymon Lukasik
Clustering constitutes a well-known problem of division of unlabelled dataset into disjoint groups of data elements. It can be tackled with standard statistical methods but also with metaheuristics, which offer more flexibility and decent performance. Th...
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Daniel Ricardo Sandoval Serrano, Juan Carlos Rincón, Julián Mejía-Restrepo, Edward Rolando Núñez-Valdez and Vicente García-Díaz
Forecasting medical costs is crucial for planning, budgeting, and efficient decision making in the health industry. This paper introduces a proposal to forecast costs through techniques such as a standard model of long short-term memory (LSTM); and patie...
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Adrián Csordás, János Pancsira, Péter Lengyel, István Füzesi and János Felföldi
The traditional global food supply chains are not just complex, but they do not support the sustainability of agriculture. The business models with the greatest growth potential are those that would allow consumers to buy more directly from producers. Be...
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Fuat Kosanoglu
The predictability of wind energy is crucial due to the uncertain and intermittent features of wind energy. This study proposes wind speed forecasting models, which employ time series clustering approaches and deep learning methods. The deep learning (LS...
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