AI-based framework to predict animal and pen feed intake in feedlot beef cattle

A recent study utilized the METABRIC breast cancer cohort to explore the effectiveness of copula-based fusion of clinical and genomic machine learning risk scores for predicting 5-year cancer-specific mortality. By modeling the joint relationship between clinical variables and genomic data, researchers aimed to enhance risk stratification beyond traditional linear methods.

A new study has introduced a synthetic data generation framework aimed at identifying pre-injury patterns in triathletes by analyzing lifestyle, recovery, and load dynamics features. This framework generates physiologically plausible athlete profiles and simulates individualized training programs while considering factors such as sleep quality and stress levels, which are often overlooked in injury prediction models.

A recent study has demonstrated the effectiveness of spatiotemporal Graph Neural Networks (GNNs) in forecasting multi-store retail sales, specifically using data from 45 Walmart locations. The research highlights the STGNN's ability to model inter-store dependencies and achieve lower forecasting errors compared to traditional methods like ARIMA, LSTM, and XGBoost.

A recent study published on arXiv systematically evaluated 12 feature scaling techniques across 14 machine learning algorithms and 16 datasets, revealing significant performance variations in models like Logistic Regression and SVMs, while ensemble methods showed robustness regardless of scaling.