[HTML][HTML] Feature selection in wind speed forecasting systems based on meta-heuristic optimization
Technology for anticipating wind speed can improve the safety and stability of power
networks with heavy wind penetration. Due to the unpredictability and instability of the wind …
networks with heavy wind penetration. Due to the unpredictability and instability of the wind …
Wind speed ensemble forecasting based on deep learning using adaptive dynamic optimization algorithm
The development and deployment of an effective wind speed forecasting technology can
improve the safety and stability of power systems with significant wind penetration. Due to …
improve the safety and stability of power systems with significant wind penetration. Due to …
Advanced ensemble model for solar radiation forecasting using sine cosine algorithm and newton's laws
As research in alternate energy sources is growing, solar radiation is catching the eyes of
the research community immensely. Since solar energy generation depends on …
the research community immensely. Since solar energy generation depends on …
[PDF][PDF] Optimized ensemble algorithm for predicting metamaterial antenna parameters
Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve
performance. Metamaterial antennas can overcome the bandwidth constraint associated …
performance. Metamaterial antennas can overcome the bandwidth constraint associated …
[PDF][PDF] Forecasting e-commerce adoption based on bidirectional recurrent neural networks
AA Salamai, AA Ageeli… - Computers, Materials & …, 2022 - cdn.techscience.cn
E-commerce refers to a system that allows individuals to purchase and sell things online.
The primary goal of e-commerce is to offer customers the convenience of not going to a …
The primary goal of e-commerce is to offer customers the convenience of not going to a …
Stacking: A novel data-driven ensemble machine learning strategy for prediction and mapping of Pb-Zn prospectivity in Varcheh district, west Iran
M Hajihosseinlou, A Maghsoudi… - Expert Systems with …, 2024 - Elsevier
Various ensemble machine learning techniques have been widely studied and implemented
to construct the predictive models in different sciences, including bagging, boosting, and …
to construct the predictive models in different sciences, including bagging, boosting, and …
A hybrid deep learning framework with CNN and Bi-directional LSTM for store item demand forecasting
In the era of ever-changing market landscape, enterprises tend to make quick and informed
decisions to survive and prosper in the competition. Decision makers within an organization …
decisions to survive and prosper in the competition. Decision makers within an organization …
[PDF][PDF] Robust Prediction of the Bandwidth of Metamaterial Antenna Using Deep Learning.
AA Abdelhamid, SR Alotaibi - Computers, Materials & Continua, 2022 - academia.edu
The design of microstrip antennas is a complex and time-consuming process, especially the
step of searching for the best design parameters. Meanwhile, the performance of microstrip …
step of searching for the best design parameters. Meanwhile, the performance of microstrip …
Ensemble and single algorithm models to handle multicollinearity of UAV vegetation indices for predicting rice biomass
Rice biomass is a biofuel's source and yield indicator. Conventional sampling methods
predict rice biomass accurately. However, these methods are destructive, time-consuming …
predict rice biomass accurately. However, these methods are destructive, time-consuming …
[PDF][PDF] An optimized ensemble model for prediction the bandwidth of metamaterial antenna
Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their
performance. Antenna size affects the quality factor and the radiation loss of the antenna …
performance. Antenna size affects the quality factor and the radiation loss of the antenna …