RaX-Crash: A Resource Efficient and Explainable Small Model Pipeline with an Application to City Scale Injury Severity Prediction

A new study introduces a Long Short-Term Memory (LSTM) model designed to predict NBA game outcomes using a comprehensive dataset spanning from the 2004-05 to 2024-25 seasons. This model utilizes an extensive sequence of 9,840 games to effectively capture evolving team dynamics and dependencies across seasons, addressing challenges faced by traditional prediction models.

A new ensemble-based machine learning model has been developed to enhance early diabetes prediction using the BRFSS dataset, which includes over 253,000 health records. The model employs techniques like SMOTE and Tomek Links to address class imbalance and achieves a strong ROC-AUC score of approximately 0.96 through various algorithms, including Random Forest and XGBoost.

A recent study has introduced a machine learning approach to predict I/O performance for machine learning training pipelines, addressing the growing issue of data I/O bottlenecks that hinder GPU utilization. By systematically benchmarking various storage backends, the research identified optimal configurations, achieving an impressive R-squared of 0.991 with the XGBoost model, which predicts I/O throughput with an average error of 11.8%.

A new study has introduced the Graph Convolutional Long Short-Term Memory Attention Network (GCN-LSTM-ATT) for detecting compensatory movements in stroke patients, utilizing skeleton data captured by a Kinect depth camera. The model demonstrated a detection accuracy of 0.8580, outperforming traditional methods such as Support Vector Machine, K-Nearest Neighbor, and Random Forest.

A new study introduces an expectation-maximisation framework for nowcasting, utilizing machine learning techniques to address the challenges posed by incomplete information in decision-making processes. This framework incorporates neural networks and XGBoost to model both the occurrence and reporting processes of events, particularly in the context of Argentinian Covid-19 data.

A novel learning-based framework has been proposed to address the Maximum Clique Problem (MCP), an NP-hard problem with significant applications. This framework integrates traditional machine learning techniques and graph neural networks, specifically utilizing a dual-channel model known as GAT-MLP, which combines a Graph Attention Network with a Multilayer Perceptron to enhance algorithm selection based on graph instance characteristics.

A comprehensive study has been conducted to evaluate the performance of five supervised machine learning models in detecting zero-day attacks, which are particularly challenging due to their unknown nature. The research aims to improve detection efficiency by addressing the imbalance in training data through techniques such as grid search and oversampling.