Graph Convolutional Long Short-Term Memory Attention Network for Post-Stroke Compensatory Movement Detection Based on Skeleton Data

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 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 new AI system has been developed to assist in the diagnosis and treatment planning for Glioblastoma Multiforme (GBM), a highly aggressive brain cancer with a low survival rate. This system employs a multi-agent reinforcement learning framework combined with an encoder-decoder architecture to identify optimal resection locations based on MRI scans and other diagnostic 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.

Twisted Convolutional Networks (TCNs) have been introduced as a new deep learning architecture designed for classifying one-dimensional data with arbitrary feature order and minimal spatial relationships. This innovative approach combines subsets of input features through multiplicative and pairwise interaction mechanisms, enhancing feature interactions that traditional convolutional methods often overlook.

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.