Predicting compressive strength of lightweight foamed concrete using extreme learning machine model
In this research, a machine learning model namely extreme learning machine (ELM) is
proposed to predict the compressive strength of foamed concrete. The potential of the ELM …
proposed to predict the compressive strength of foamed concrete. The potential of the ELM …
Computational intelligence in wave energy: Comprehensive review and case study
Wind-generated wave energy is a renewable energy source that exhibits a huge potential
for sustainable growth. The design and deployment of wave energy converters at a given …
for sustainable growth. The design and deployment of wave energy converters at a given …
A novel prediction method based on the support vector regression for the remaining useful life of lithium-ion batteries
Q Zhao, X Qin, H Zhao, W Feng - Microelectronics Reliability, 2018 - Elsevier
Traditional approaches to lithium-ion battery health management mostly focus on the state of
charge (SOC) estimation issues, whereas the state of health (SOH) estimation is also critical …
charge (SOC) estimation issues, whereas the state of health (SOH) estimation is also critical …
Recognition and detection of tea leaf's diseases using support vector machine
S Hossain, RM Mou, MM Hasan… - 2018 IEEE 14th …, 2018 - ieeexplore.ieee.org
Tea is a popular beverage all around the world, and in Bangladesh the cultivation of tea
plays a vital role. Many diseases affect the proper growth of tea leaves leading to its …
plays a vital role. Many diseases affect the proper growth of tea leaves leading to its …
Using atmospheric inputs for Artificial Neural Networks to improve wind turbine power prediction
A robust machine learning methodology is used to generate a site-specific power-curve of a
full-scale isolated wind turbine operating in an atmospheric boundary layer to drastically …
full-scale isolated wind turbine operating in an atmospheric boundary layer to drastically …
Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization–Extreme learning machine approach
S Salcedo-Sanz, A Pastor-Sánchez, L Prieto… - Energy Conversion and …, 2014 - Elsevier
This paper presents a novel approach for short-term wind speed prediction based on a
Coral Reefs Optimization algorithm (CRO) and an Extreme Learning Machine (ELM), using …
Coral Reefs Optimization algorithm (CRO) and an Extreme Learning Machine (ELM), using …
A novel ensemble learning for post-processing of NWP Model's next-day maximum air temperature forecast in summer using deep learning and statistical approaches
A reliable and accurate extreme air temperature in summer is necessary to prepare for and
respond to thermal disasters such as heatstroke and power outages. The numerical weather …
respond to thermal disasters such as heatstroke and power outages. The numerical weather …
A hybrid classifier combining SMOTE with PSO to estimate 5-year survivability of breast cancer patients
In this study, we propose a set of new algorithms to enhance the effectiveness of
classification for 5-year survivability of breast cancer patients from a massive data set with …
classification for 5-year survivability of breast cancer patients from a massive data set with …
Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms
Long-term air temperature prediction is of major importance in a large number of
applications, including climate-related studies, energy, agricultural, or medical. This paper …
applications, including climate-related studies, energy, agricultural, or medical. This paper …
[HTML][HTML] Forecasting and optimizing dual media filter performance via machine learning
Four different machine learning algorithms, including Decision Tree (DT), Random Forest
(RF), Multivariable Linear Regression (MLR), Support Vector Regressions (SVR), and …
(RF), Multivariable Linear Regression (MLR), Support Vector Regressions (SVR), and …