[HTML][HTML] Feature selection in wind speed forecasting systems based on meta-heuristic optimization

ESM El-Kenawy, S Mirjalili, N Khodadadi… - Plos one, 2023 - journals.plos.org
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 …

Wind speed ensemble forecasting based on deep learning using adaptive dynamic optimization algorithm

A Ibrahim, S Mirjalili, M El-Said, SSM Ghoneim… - IEEE …, 2021 - ieeexplore.ieee.org
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 …

Advanced ensemble model for solar radiation forecasting using sine cosine algorithm and newton's laws

ESM El-Kenawy, S Mirjalili, SSM Ghoneim… - IEEE …, 2021 - ieeexplore.ieee.org
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 …

[PDF][PDF] Optimized ensemble algorithm for predicting metamaterial antenna parameters

EM El-kenawy, A Ibrahim, S Mirjalili… - Computers …, 2022 - feng.stafpu.bu.edu.eg
Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve
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 …

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 …

A hybrid deep learning framework with CNN and Bi-directional LSTM for store item demand forecasting

RV Joseph, A Mohanty, S Tyagi, S Mishra… - Computers and …, 2022 - Elsevier
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 …

[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 …

Ensemble and single algorithm models to handle multicollinearity of UAV vegetation indices for predicting rice biomass

R Derraz, FM Muharam, K Nurulhuda, NA Jaafar… - … and Electronics in …, 2023 - Elsevier
Rice biomass is a biofuel's source and yield indicator. Conventional sampling methods
predict rice biomass accurately. However, these methods are destructive, time-consuming …

[PDF][PDF] An optimized ensemble model for prediction the bandwidth of metamaterial antenna

A Ibrahim, H Abutarboush, A Mohamed… - … , Materials & Continua, 2022 - researchgate.net
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 …