Deep learning models have revolutionized numerous fields, yet their decision-making
processes often remain opaque, earning them the characterization of “black-box” models …

In the past decade, human digital twins (HDTs) attracted attention in both digital twin (DT)
applications and beyond. In this paper, we discuss the concept and the development of …

Computational cognitive models are a fundamental tool in behavioral neuroscience. They
embody in software precise hypotheses about the cognitive mechanisms underlying a …

Highlights•Ammonia nitrogen LC 50 for American black bass was explored.•Fish mortality
under ammonia nitrogen stress predicted by machine learning model.•Dynamic modeling …

Reinforcement learning (RL) models are used extensively to study human behavior. These
rely on normative models of behavior and stress interpretability over predictive capabilities …

Normative frameworks such as Bayesian inference and reward-based learning are useful
tools for explaining the fundamental principles of adaptive behavior. However, their ability to …

The adoption of “artificial intelligence (AI) in drug discovery”, where AI is used in the process
of pharmaceutical research and development, is progressing. By using the ability to process …

The assessment of urban wealthiness is fundamental to effective urban planning and
development. However, conventional methodologies often rely on aggregated datasets …

Online reviews play a crucial role in comprehending user rating behavior and improving
personalized recommendations in e-commerce. However, existing review-based …

Active reinforcement learning enables dynamic prediction and control, where one should not
only maximize rewards but also minimize costs such as of inference, decisions, actions, and …