A Comprehensive Study of Supervised Machine Learning Models for Zero-Day Attack Detection: Analyzing Performance on Imbalanced Data

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 recent study highlights the integration of Machine Learning (ML) and Deep Learning (DL) with Digital Signal Processing (DSP) and Digital Image Processing (DIP), showcasing transformative applications in image enhancement, filtering techniques, and pattern recognition. The research emphasizes the use of frameworks like Discrete Fourier Transform (DFT) and Python for real-time data processing.

A recent study highlights the significance of Machine Unlearning in enhancing privacy within Machine Learning models. By enabling the selective removal of data from trained models, techniques such as Naive Retraining and Exact Unlearning via the SISA framework are evaluated for their computational costs and feasibility using the HSpam14 dataset. The research emphasizes the integration of unlearning principles into Positive Unlabeled Learning to tackle challenges posed by partially labeled datasets.

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.

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 published in Nature — Machine Learning highlights the application of transformer-based deep learning techniques to enhance discoveries in genome-wide association studies (GWAS) related to migraines. This innovative approach aims to improve the understanding of genetic factors contributing to migraine susceptibility.

China is positioning itself as a leader in global artificial intelligence (AI) governance, aiming to establish comprehensive regulations that could shape international standards and practices in AI. This initiative reflects China's strategic intent to influence the global AI landscape and ensure its technologies align with its national interests.

SIDISH has integrated single-cell and bulk transcriptomics to identify high-risk cells, utilizing in silico perturbation to guide precision therapeutics. This innovative approach aims to enhance the understanding of cellular behavior and improve treatment strategies in precision medicine.