Ensemble reinforcement learning: A survey
Reinforcement Learning (RL) has emerged as a highly effective technique for addressing
various scientific and applied problems. Despite its success, certain complex tasks remain …
various scientific and applied problems. Despite its success, certain complex tasks remain …
Review of semantic segmentation of medical images using modified architectures of UNET
M Krithika Alias AnbuDevi, K Suganthi - Diagnostics, 2022 - mdpi.com
In biomedical image analysis, information about the location and appearance of tumors and
lesions is indispensable to aid doctors in treating and identifying the severity of diseases …
lesions is indispensable to aid doctors in treating and identifying the severity of diseases …
Short-term multi-step wind power forecasting based on spatio-temporal correlations and transformer neural networks
Spatio-temporal wind power forecasting is significant to the stability of electric power
systems. However, the accuracy of power forecasting results is easily impaired by the …
systems. However, the accuracy of power forecasting results is easily impaired by the …
An online reinforcement learning-based energy management strategy for microgrids with centralized control
To address the issue of significant unpredictability and intermittent nature of renewable
energy sources, particularly wind and solar power, this paper introduces a novel …
energy sources, particularly wind and solar power, this paper introduces a novel …
X-ray image based COVID-19 detection using evolutionary deep learning approach
SMJ Jalali, M Ahmadian, S Ahmadian… - Expert Systems with …, 2022 - Elsevier
Radiological methodologies, such as chest x-rays and CT, are widely employed to help
diagnose and monitor COVID-19 disease. COVID-19 displays certain radiological patterns …
diagnose and monitor COVID-19 disease. COVID-19 displays certain radiological patterns …
A spatio-temporal sequence-to-sequence network for traffic flow prediction
Spatio-temporal prediction has drawn much attention given its wide application, of which
traffic flow prediction is a typical task. Within the vision of smart cities, traffic flow prediction …
traffic flow prediction is a typical task. Within the vision of smart cities, traffic flow prediction …
Short-term wind farm cluster power prediction based on dual feature extraction and quadratic decomposition aggregation
Z Qu, X Hou, J Li, W Hu - Energy, 2024 - Elsevier
The intermittency and uncertainty of wind energy affect the accuracy of wind power
prediction, which is not conducive to the safe and stable operation of the power system …
prediction, which is not conducive to the safe and stable operation of the power system …
RDERL: Reliable deep ensemble reinforcement learning-based recommender system
Recommender systems (RSs) have been employed for many real-world applications
including search engines, social networks, and information retrieval systems as powerful …
including search engines, social networks, and information retrieval systems as powerful …
A review of modern wind power generation forecasting technologies
WC Tsai, CM Hong, CS Tu, WM Lin, CH Chen - Sustainability, 2023 - mdpi.com
The prediction of wind power output is part of the basic work of power grid dispatching and
energy distribution. At present, the output power prediction is mainly obtained by fitting and …
energy distribution. At present, the output power prediction is mainly obtained by fitting and …
Solar irradiance forecasting using a novel hybrid deep ensemble reinforcement learning algorithm
Solar irradiance forecasting is a major priority for the power transmission systems in order to
generate and incorporate the performance of massive photovoltaic plants efficiently. As …
generate and incorporate the performance of massive photovoltaic plants efficiently. As …