Specifically, we propose a novel algorithm named PPO-DQN which merging the proximal policy optimization (PPO) and deep Q-network (DQN) algorithm to effectively solve the optimization problem with both ...

Simulation results demonstrate that the proposed DRL-based utility optimization algorithm achieves faster inference speed compared to deep Q network and effectively improves the task processing rate, ...

We used DL methods based on images to distinguish pulmonary infections. A machine learning (ML) model for risk interpretation was developed using key imaging (learned from the DL methods) and clinical ...

We use genetic algorithms for the optimization of training set composition consisting of tens of thousands of small organic molecules. The resulting machine learning models are considerably more ...

To investigate the intrinsic role of EPS in fouling, a predictive membrane fouling model was developed using a supervised learning algorithm trained on experimental EPS data sets. After hyperparameter ...