Long-Sequence LSTM Modeling for NBA Game Outcome Prediction Using a Novel Multi-Season Dataset

CourtMotion introduces a new spatiotemporal modeling framework designed to analyze and predict basketball game events by utilizing skeletal tracking data. The framework employs Graph Neural Networks to capture intricate motion patterns and a Transformer architecture to model player interactions, ultimately linking physical movements to tactical basketball events such as passes and shots.

RaX-Crash has been developed as a resource-efficient and explainable small model pipeline aimed at predicting injury severity from motor vehicle collisions in New York City, utilizing a dataset with over one hundred thousand records. The model employs compact tree-based ensembles, specifically Random Forest and XGBoost, achieving notable accuracy compared to small language models.

A recent study has introduced a hybrid framework that integrates Long Short-Term Memory (LSTM) networks with the Merton-Lévy jump-diffusion model for financial forecasting. This approach utilizes the Grey Wolf Optimizer for hyperparameter tuning and evaluates its predictive performance against benchmark models using real-world datasets, including Brent oil prices and the STOXX 600 index.

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 novel approach named PRIMRose has been introduced to predict energy values for individual residues in proteins with double InDel mutations, utilizing a Convolutional Neural Network architecture. This method focuses on the localized energetic impacts of mutations rather than global energy shifts, providing deeper insights into structural and functional disruptions caused by these mutations.

A new real-time air pollution prediction model has been developed in Daegu, Korea, utilizing spatiotemporal big data collected from sensors installed on taxis. This model employs a Convolutional Neural Network (CNN) for spatial distribution analysis and integrates Long Short-Term Memory (LSTM) units to account for temporal data and other influencing factors like weather conditions.

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 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.