This manuscript provides important information on the neurodynamics of emotional processing while participants were watching movie clips. This work provides convincing results in deciphering the ...

When a natural disaster strikes, first responder managers face a flood of data — from drones, sensors, cameras, satellites, police, firefighters, and citizens — that must be sorted, secured, and ...

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...

ABSTRACT: This paper investigates the application of machine learning techniques to optimize complex spray-drying operations in manufacturing environments. Using a mixed-methods approach that combines ...

Abstract: This study proposes a Dynamic Bayesian Network (DBN)-based model for evaluating the reliability of optical networks, effectively quantifying the state changes and reliability of optical ...

Dynamic functional network connectivity (dFNC) assesses temporal fluctuations in functional connectivity (FC) during magnetic resonance imaging (MRI), capturing transient changes in neural activity.

Abstract: Dynamic Bayesian Networks (DBNs) are useful tools for modelling complex systems whose network representations can be elicited a priori or learnt from data. In this paper, a maximum ...

Department of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, 1218 S 5th Ave, Monrovia, California 91016, United States Irell and Manella Graduate School of ...

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