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. This research leveraged convolutional neural networks (CNNs) and transfer learning (TL) classification techniques, testing various architectures from the EfficientNet family and two ResNet models for classifying sky images. Cross-validation methods were applied across different experiments, where the most favorable outcome was achieved with the EfficientNetV2-B1 and EfficientNetV2-B2 models boasting a mean Accuracy of 98.09%. This study underscores the efficacy of the architectures employed for sky image classification, while also highlighting the models yielding the best results.

The emergence of the COVID-19 pandemic in 2019 and its rapid global spread put healthcare systems around the world to the test. This crisis created an unprecedented level of stress in hospitals, exacerbating the already complex task of healthcare management. As a result, it led to a tragic increase in mortality rates and highlighted the urgent need for advanced predictive tools to support decision-making. To address these critical challenges, this research aims to develop and implement a predictive system capable of predicting pandemic evolution with accuracy (in terms of Mean Absolute error (MAE), Root Mean Square Error (RMSE), R², and Mean Absolute Percentage Error (MAPE)) and low computational and economic cost. It uses a set of interconnected Long Short Term-memory (LSTM) with double bidirectional LSTM (BiLSTM) layers together with a novel preprocessing based on future time windows. This model accurately predicts COVID-19 cases and hospital occupancy over long periods of time using only 40% of the set to train. This results in a long-term prediction where each day we can query the cases for the next three days with very little data. The data utilized in this analysis were obtained from the “Hospital Insular” in Gran Canaria, Spain. These data describe the spread of the coronavirus disease (COVID-19) from its initial emergence in 2020 until March 29, 2022. The results show an improvement in MAE (< 161), RMSE (< 405), and MAPE (> 0.20) compared to other studies with similar conditions. This would be a powerful tool for the healthcare system, providing valuable information to decision-makers, allowing them to anticipate and strategize for possible scenarios, ultimately improving public health outcomes and optimizing the allocation of healthcare and economic resources.

Pattern recognition is becoming increasingly important topic in all sectors of society. From the optimization of processes in the industry to the detection and diagnosis of diseases in medicine. Brain-computer interfaces are introduced in this chapter. Systems capable of analyzing brain signal patterns, processing and interpreting them through machine and deep learning algorithms. In this chapter, a hybrid deep/machine learning ensemble system for brain pattern recognition is proposed. It is capable to recognize patterns and translate the decisions to BCI systems. For this, a public database (Physionet) with data of motor tasks and mental tasks is used. The development of this chapter consists of a brief summary of the state of the art, the presentation of the model together with some results and some promising conclusions.