Predictive toxicology is a crucial field that focuses on assessing the potential toxic effects of chemical compounds, playing an essential role in drug ...

The existing dementia risk models are limited to known risk factors and traditional statistical methods. We aimed to employ machine learning (ML) to develop a novel dementia prediction model by ...

Predictive maintenance (PdM) has become a critical strategy for improving the efficiency and reliability of industrial machinery. Integrating machine learning methods into a PdM system provides a ...

A Situation Based Predictive Approach for Cybersecurity Intrusion Detection and Prevention Using Machine Learning and Deep Learning Algorithms in Wireless Sensor Networks of Industry 4.0 ...

Explainable machine learning models were used to identify predictive features, with SHapley Additive exPlanation (SHAP) analysis ranking variable importance. Two-way fixed-effect logistic regression ...

Learn about data quality, model evaluation, model explainability, and model reliability aspects to consider when working with AI and machine learning models.

This study introduces a Q-learning-based nonlinear model predictive control (QL-NMPC) framework for temperature control in batch reactors. A reinforcement learning agent is trained in simulation to ...

Machine learning and artificial intelligence (AI/ML) methods are beginning to have significant impact in chemistry and condensed matter physics. For example, deep learning methods have demonstrated ...

In addition, in a network model consisting of distinct excitatory and inhibitory neural populations, our learning rule predicts the emergence of two types of inhibitory connections with different ...