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The Physics-Informed Neural Network Gravity Model: Generation III
Scientific machine learning and the advent of the Physics-Informed Neural Network (PINN) show considerable potential in their capacity to identify solutions to complex differential equations.
Predicting Long-term Renal Impairment in Post-COVID-19 Patients with Machine Learning Algorithms
This study, conducted with a cohort of 821 post-COVID-19 patients from diverse regions of Iraq across the years 2021, 2022, and 2023, endeavors to predict the risk of long-term renal impairment using advanced machine learning algorithms.
Characterizing Pulmonary Fibrosis Patterns in Post-COVID-19 Patients through Machine Learning Algorithms
The COVID-19 pandemic has left a lasting impact on global healthcare systems, with increasing evidence of pulmonary fibrosis emerging as a post-infection complication.
Long-term Neurological Sequelae in Post-COVID-19 Patients: A Machine Learning Approach to Predict Outcomes
In conclusion, this study sheds light on the prevalence of long-term neurological complications in post-COVID-19 patients and demonstrates the potential of machine learning in predicting outcomes, thereby contributing to enhanced patient management and better health outcomes.
Recursive Sparse Pseudo-input Gaussian Process SARSA
The class of Gaussian Process (GP) methods for Temporal Difference learning has shown promise for data-efficient model-free Reinforcement Learning.
Sparse Gaussian Process Temporal Difference Learning for Marine Robot Navigation
Our results show SPGP-SARSA can outperform the state-of-the-art sparse method, replicate the prediction quality of its exact counterpart, and be applied to solve underwater navigation tasks.
