Finding Pre-Injury Patterns in Triathletes from Lifestyle, Recovery and Load Dynamics Features

A recent study highlights the challenges of estimating prediction uncertainty in active learning campaigns for materials discovery, indicating that uncalibrated out-of-distribution uncertainty quantification may hinder model performance. The research evaluates various uncertainty estimation methods using ensembles of ALIGNN, eXtreme Gradient Boost, Random Forest, and Neural Network architectures, focusing on tasks related to solubility, bandgap, and formation energy predictions.

An AI-based framework has been developed to predict feed intake for individual beef cattle and pen-level aggregation, utilizing data from 19 experiments conducted at the Nancy M. Cummings Research Extension & Education Center in Carmen, ID, alongside environmental data from AgriMet Network weather stations. This framework aims to leverage big data generated by electronic feeding systems to enhance precision livestock farming practices.

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 new study introduces Upstream Probabilistic Meta-Imputation (UPMI) as a novel strategy for classifying pediatric pancreatitis, a complex inflammatory condition. This method leverages machine learning techniques to enhance diagnostic accuracy by utilizing a low-dimensional meta-feature space, addressing challenges posed by limited sample sizes and the intricacies of multimodal imaging.

A new study introduces the Extracted Morphological Feature Engineered (EMFE) pipeline, a lightweight machine learning approach for malaria classification that achieves performance levels comparable to deep learning models while requiring significantly less computational power. This method utilizes the NIH Malaria Cell Images dataset, focusing on simple cell morphology features such as non-background pixels and holes within cells.

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 recent study has enhanced breast cancer prediction by utilizing large language models (LLMs) to infer the likelihood of confounding diseases such as diabetes, obesity, and cardiovascular disease from routine clinical data. This innovative approach has shown to improve the performance of Random Forest models, with notable enhancements from models like Gemma and Llama, indicating a significant advancement in predictive analytics for breast cancer.

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.