[HTML][HTML] Rice yield forecasting using hybrid quantum deep learning model
In recent advancements in agricultural technology, quantum mechanics and deep learning
integration have shown promising potential to revolutionize rice yield forecasting methods …
integration have shown promising potential to revolutionize rice yield forecasting methods …
Quantum Computational Intelligence Techniques: A Scientometric Mapping
M Arora, K Gupta - Archives of Computational Methods in Engineering, 2024 - Springer
Computational intelligence has previously demonstrated its existence beyond the limitations
of binary variables and Turing Machines. Using quantum concepts, Deutsch (1985) and …
of binary variables and Turing Machines. Using quantum concepts, Deutsch (1985) and …
Developing an interpretable wind power forecasting system using a transformer network and transfer learning
C Tian, T Niu, T Li - Energy Conversion and Management, 2025 - Elsevier
Accurate wind power forecasting is crucial for enhancing the stability and security of power
grid operations and scheduling. However, previous studies have primarily focused on data …
grid operations and scheduling. However, previous studies have primarily focused on data …
Complex-valued artificial hummingbird algorithm for global optimization and short-term wind speed prediction
L Feng, Y Zhou, Q Luo, Y Wei - Expert Systems with Applications, 2024 - Elsevier
Environmental pollution and energy depletion have spurred the exploration of renewable
energy sources. Wind energy, with its sustainability and eco-friendliness, stands out as a …
energy sources. Wind energy, with its sustainability and eco-friendliness, stands out as a …
Decomposition based deep projection-encoding echo state network for multi-scale and multi-step wind speed prediction
T Li, Z Guo, Q Li - Expert Systems with Applications, 2024 - Elsevier
Accurate wind speed forecasting is essential to improve the scheduling and the utilization
ratio of wind power. However, it is challenging to accurately forecast the wind speed …
ratio of wind power. However, it is challenging to accurately forecast the wind speed …
Using stacking ensemble learning to predict multi-step wind speed based on wavelet transformation, two-steps feature selection method, and neural networks
Wind energy is gaining attention in power sector. However, the instability of wind speed
(WS) negatively affects the incorporation of wind energy into the power grid. This paper …
(WS) negatively affects the incorporation of wind energy into the power grid. This paper …
Hybrid Quantum Convolutional Neural Network for Defect Detection in a Wind Turbine Gearbox
Convolutional neural networks (CNNs) have been acknowledged for their effectiveness in
vibration-based fault detection. However, when used to model high-dimensional vibration …
vibration-based fault detection. However, when used to model high-dimensional vibration …
[PDF][PDF] Time-Stratified Analysis of Electricity Consumption: A Regression and Neural Network Approach in the Context of Turkey
S Yi̇ği̇t, S Turgay, Ç Cebeci… - WSEAS Transactions on …, 2024 - researchgate.net
This study aims to apply seasonality and temporal effects in the analysis of electricity
consumption in Turkey as a case mixed with regression and neural network methodologies …
consumption in Turkey as a case mixed with regression and neural network methodologies …
Air quality prediction based on quantum activation function optimized hybrid quantum classical neural network
Y Dong, F Li, T Zhu, R Yan - Frontiers in Physics, 2024 - frontiersin.org
Accurate prediction of air quality index is a challenging task, in order to solve the gradient
problem of traditional neural network methods in the time series prediction process as well …
problem of traditional neural network methods in the time series prediction process as well …
A Multiple-Location Modeling Scheme for Physics-Regularized Networks: Recurrent Forecasting of Fixed-Location Buoy Observations
Reliable oceanic and climate analysis depend on high-quality sensor readings, yet these
systems commonly encounter significant sensor limitations, leading to missing data …
systems commonly encounter significant sensor limitations, leading to missing data …