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 Canary Islands are considered a hotspot of biodiversity and have high levels of endemicity, including endemic reptile species. Nowadays, some invasive alien species of reptiles are proliferating with no control in different parts of the territory, creating a dangerous situation for the ecosystems of this archipelago. Despite the fact that the regional authorities have initiated actions to try to control the proliferation of invasive species, the problem has not been solved as it depends on sporadic sightings, and it is impossible to determine when these species appear. Since no studies for automatically identifying certain species of reptiles endemic to the Canary Islands have been found in the current state-of-the-art, from the Signals and Communications Department of the Las Palmas de Gran Canaria University (ULPGC), we consider the possibility of developing a detection system based on automatic species recognition using deep learning (DL) techniques. So this research conducts an initial identification study of some species of interest by implementing different neural network models based on transfer learning approaches. This study concludes with a comparison in which the best performance is achieved by integrating the EfficientNetV2B3 base model, which has a mean Accuracy of 98.75%.

Intensive Care Unit (ICU) readmissions following Intracerebral Hemorrhage (ICH) are associated with increased mortality and resource burden. Current prediction models predominantly rely on static admission features, failing to capture the temporal evolution of physiological instability. This study proposes a novel deep learning framework to predict ICU readmission by leveraging high-resolution time-series data from the MIMIC-III and MIMIC-IV databases. We developed a Stacked Gated Recurrent Unit (GRU) Architecture Ensemble, integrated with Time-series Generative Adversarial Networks (TimeGAN) to address the inherent class imbalance of readmission events. Our model achieved a state-of-the-art Area Under the Receiver Operating Characteristic Curve (AUC) of 0.912, significantly outperforming traditional machine learning baselines and static feature models. The sensitivity of 88.1% highlights the model’s efficacy in minimizing unsafe premature discharges. Furthermore, interpretability analysis using SHAP values identified Length of Stay, MELD Score, and Monocytes as critical predictors, revealing that readmission risk is driven by a complex interplay between systemic organ dysfunction and inflammatory response. These findings demonstrate that incorporating temporal dynamics and generative data augmentation significantly enhances risk stratification, offering a robust clinical decision support tool to optimize discharge timing in neurocritical care.

The Canary Islands, internationally recognized as a biodiversity hot spot, possess unique ecological characteristics including endemic reptile species that face substantial threats from invasive alien species. Particularly, concerning is the California kingsnake ( Lampropeltis californiae ), which exhibits remarkable adaptability and inflicts severe ecological damage on endangered endemic fauna. This proposal aims to address biodiversity conservation challenges in the Canary Islands ecosystem by advancing reptile species classification through the implementation of Swin Transformer architectures. The integration of these advanced neural network architectures with conservation biology provides an automated, precise tool for species identification across different taxonomic levels that can enhance monitoring and control strategies for biodiversity preservation worldwide. This technical approach addresses urgent conservation requirements in the Canary Islands while simultaneously establishing a methodological framework applicable to other ecological contexts facing comparable biodiversity threats. The methodology employed pre-trained Swin Transformer models, initially developing fine-grained reptile species discrimination capabilities through hyperparameter optimization on a limited-scale database (160 images, 40 per species) using nested cross-validation to ensure statistical rigor and independence. The optimized configuration was subsequently transferred to a substantially larger database (4,400 images) for binary classification distinguishing snake from non-snake reptiles, where training on only 400 samples achieved 99.38% accuracy on 4,000 independent test samples, directly addressing the invasive alien species monitoring challenge. The Swin-B Transformer model demonstrated robust performance, achieving up to 100% accuracy in multiclass species classification and 99.38% accuracy in the challenging, taxonomically broader binary snake versus non-snake classification. These results establish the transferability and scalability of the hyperparameter optimization methodology across datasets of substantially different magnitudes.