Publications by authors named "Nabil I Ajali-Hernandez"
Article Synopsis
- The COVID-19 pandemic severely strained global healthcare systems, resulting in higher mortality rates and revealing the need for better predictive tools for healthcare decision-making.
- This research focuses on developing an advanced predictive system using interconnected Long Short Term Memory (LSTM) networks to accurately forecast COVID-19 cases and hospital occupancy with minimal data and low costs.
- The study used data from a hospital in Gran Canaria and achieved improved predictive accuracy metrics (MAE, RMSE, MAPE) compared to other studies, offering a valuable resource for healthcare decision-makers to enhance public health outcomes and resource allocation.
Cloudy conditions at a local scale pose a significant challenge for forecasting renewable energy generation through photovoltaic panels. Consequently, having real-time knowledge of sky conditions becomes highly valuable. This information could inform decision-making processes in system operations, such as determining whether conditions are favorable for activating a standalone system requiring a minimum level of radiation or whether sky conditions might lead to higher energy consumption than generation during adverse cloudy conditions.
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