Early prediction of patients in need of admission to the intensive care unit (ICU) is essential for maximizing the use of available hospital resources and enhancing the quality of patient care outcomes. This work uses the Covid19MPD Dataset to predict ICU admissions based on various machine learning techniques such as Random Forest, Support Vector Machine, Gradient Boosting, and Multi-Layer Perceptron alongside Explainable Artificial Intelligence (XAI) approaches. Our findings show that the Gradient Boosting model achieved the best accuracy at 97.49% and an F1 score of 71% for ICU admissions. Notably, the study finds that age and pneumonia are important predictors, with patients 45 years and older who come with COVID-19 and pneumonia having a much higher chance of needing ICU care. These findings highlight how important it is to use machine learning models in clinical settings in order to improve ICU admission prediction and facilitate prompt medical intervention.

Flamingos are known for their vibrant pink and reddish hues, which are not merely aesthetic but indicative of their overall health and diet. These colors are derived from carotenoid pigments in their food sources, making coloration a vital marker for monitoring their well-being and environmental conditions. This study introduces a two-stage classification methodology designed to safeguard flamingo populations by leveraging deep learning techniques. Convolutional Neural Networks (CNNs) are used for both shape and color detection, ensuring accurate identification of flamingos and insights into their health status. Simulation results demonstrated the CNNs model’s effectiveness, making it a valuable resource for wildlife conservation efforts aimed at preserving flamingo habitats. The first stage employs digital classification filters to distinguish flamingo images from other species, achieving an accuracy of 97.52%, while the second stage refines these detections through color analysis with an accuracy of 86.27%. This approach promises to mark a significant advancement in wildlife conservation, offering reliable methods for assessing and managing flamingo populations in their natural environments.