DDFC: deep learning approach for deep feature extraction and classification of brain tumors using magnetic resonance imaging in E-healthcare system
This research explores the use of gated recurrent units (GRUs) for automated brain tumor
detection using MRI data. The GRU model captures sequential patterns and considers …
detection using MRI data. The GRU model captures sequential patterns and considers …
Short-term load forecasting method based on feature preference strategy and LightGBM-XGboost
X Yao, X Fu, C Zong - IEEE Access, 2022 - ieeexplore.ieee.org
Short term load forecasting is one of the important problems in power system. Accurate
forecasting results can improve the flexibility of power market and resource utilization …
forecasting results can improve the flexibility of power market and resource utilization …
[HTML][HTML] Toward explainable electrical load forecasting of buildings: A comparative study of tree-based ensemble methods with Shapley values
Electrical load forecasting of buildings is crucial in designing an energy operation strategy
for smart city realization. Although artificial intelligence techniques have demonstrated …
for smart city realization. Although artificial intelligence techniques have demonstrated …
A short-term electric load forecast method based on improved sequence-to-sequence GRU with adaptive temporal dependence
Accurate and efficient short-term electric load forecast (STLF) is essential for power systems'
reliable and economical operation. The temporal dependence of actual load exhibits …
reliable and economical operation. The temporal dependence of actual load exhibits …
[HTML][HTML] A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks
Q Yin, C Han, A Li, X Liu, Y Liu - Sustainability, 2024 - mdpi.com
Building energy consumption prediction models are powerful tools for optimizing energy
management. Among various methods, artificial neural networks (ANNs) have become …
management. Among various methods, artificial neural networks (ANNs) have become …
Short-term streamflow forecasting using hybrid deep learning model based on grey wolf algorithm for hydrological time series
The effects of develo** technology and rapid population growth on the environment have
been expanding gradually. Particularly, the growth in water consumption has revealed the …
been expanding gradually. Particularly, the growth in water consumption has revealed the …
Forecasting smart home electricity consumption using vmd-bi-gru
Due to its important role in smart grids, power system management, and smart buildings,
energy consumption forecasting has gained a lot of interest in recent years, further achieving …
energy consumption forecasting has gained a lot of interest in recent years, further achieving …
[HTML][HTML] BiGTA-Net: A hybrid deep learning-based electrical energy forecasting model for building energy management systems
The growth of urban areas and the management of energy resources highlight the need for
precise short-term load forecasting (STLF) in energy management systems to improve …
precise short-term load forecasting (STLF) in energy management systems to improve …
Robust building energy consumption forecasting using an online learning approach with R ranger
Recently, the online learning-based stacking ensemble approach has yielded satisfactory
short-term load forecasting (STLF) because it can effectively reflect recent building energy …
short-term load forecasting (STLF) because it can effectively reflect recent building energy …
Real-time prediction of rate of penetration by combining attention-based gated recurrent unit network and fully connected neural networks
C Zhang, X Song, Y Su, G Li - Journal of Petroleum Science and …, 2022 - Elsevier
Data-driven models are widely used to predict rate of penetration. However, there are still
challenges on real-time predictions considering influences of formation properties and bit …
challenges on real-time predictions considering influences of formation properties and bit …