Deep reinforcement learning for the dynamic and uncertain vehicle routing problem

W Pan, SQ Liu - Applied Intelligence, 2023 - Springer
Accurate and real-time tracking for real-world urban logistics has become a popular
research topic in the field of intelligent transportation. While the routing of urban logistic …

A machine learning-assisted numerical predictor for compressive strength of geopolymer concrete based on experimental data and sensitivity analysis

AT Huynh, QD Nguyen, QL Xuan, B Magee… - Applied Sciences, 2020 - mdpi.com
Geopolymer concrete offers a favourable alternative to conventional Portland concrete due
to its reduced embodied carbon dioxide (CO2) content. Engineering properties of …

Multitask asynchronous metalearning for few-shot anomalous node detection in dynamic networks

Y Hong, C Shi, J Chen, H Wang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Few-shot anomalous node detection in dynamic networks has been extensively investigated
in the field of research. In this few-shot scenario, the detection of these anomalous nodes is …

WATuning: a workload-aware tuning system with attention-based deep reinforcement learning

JK Ge, YF Chai, YP Chai - Journal of Computer Science and Technology, 2021 - Springer
Configuration tuning is essential to optimize the performance of systems (eg, databases, key-
value stores). High performance usually indicates high throughput and low latency. At …

A general framework for optimising cost-effectiveness of pandemic response under partial intervention measures

QD Nguyen, M Prokopenko - Scientific Reports, 2022 - nature.com
The COVID-19 pandemic created enormous public health and socioeconomic challenges.
The health effects of vaccination and non-pharmaceutical interventions (NPIs) were often …

[HTML][HTML] Compressive strength analysis of fly ash-based geopolymer concrete using machine learning approaches

DA Emarah - Results in Materials, 2022 - Elsevier
Cement is the primary component of concrete, an extensively used building material. When
cement is manufactured or utilized, an excessive amount of gas is emitted into the …

Interpreting a deep reinforcement learning model with conceptual embedding and performance analysis

Y Dai, H Ouyang, H Zheng, H Long, X Duan - Applied Intelligence, 2023 - Springer
The weak interpretability of the deep reinforcement learning (DRL) model becomes a
serious impediment to the application of DRL agents in certain areas requiring high …

Motor Interaction Control Based on Muscle Force Model and Depth Reinforcement Strategy

H Liu, H Zhang, J Lee, P Xu, I Shin, J Park - Biomimetics, 2024 - mdpi.com
The current motion interaction model has the problems of insufficient motion fidelity and lack
of self-adaptation to complex environments. To address this problem, this study proposed to …

[HTML][HTML] Small-Sample Data Pricing Based on Data Augmentation and Meta-Learning

J Shen, Y Yang, F Xiao - Electronics, 2024 - mdpi.com
Data trading platforms play a crucial role in facilitating data circulation and promoting the
sustainable allocation of data resources. Establishing a transparent, fair, and efficient pricing …

Discussion on complexity and accuracy of high-performance concrete's compressive strength deep learning models

HV Pham, MN Dinh, S Luong… - … on Computing and …, 2022 - ieeexplore.ieee.org
A non-linear relationship has been observed between the cementitious components of high-
performance concrete and the concrete properties. This study proposes a deep learning …