Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions
Adaptation and innovation are extremely important to the manufacturing industry. This
development should lead to sustainable manufacturing using new technologies. To promote …
development should lead to sustainable manufacturing using new technologies. To promote …
A survey of machine learning models in renewable energy predictions
The use of renewable energy to reduce the effects of climate change and global warming
has become an increasing trend. In order to improve the prediction ability of renewable …
has become an increasing trend. In order to improve the prediction ability of renewable …
Classification based on decision tree algorithm for machine learning
B Charbuty, A Abdulazeez - Journal of Applied Science and Technology …, 2021 - jastt.org
Decision tree classifiers are regarded to be a standout of the most well-known methods to
data classification representation of classifiers. Different researchers from various fields and …
data classification representation of classifiers. Different researchers from various fields and …
Solar photovoltaic Maximum Power Point Tracking controller optimization using Grey Wolf Optimizer: A performance comparison between bio-inspired and traditional …
Solar photovoltaic systems are widely used; however, their performance is bound to weather
conditions, depending on irradiation, temperature, and the effect of shadows. Maximum …
conditions, depending on irradiation, temperature, and the effect of shadows. Maximum …
Selective harmonic elimination in inverters using bio-inspired intelligent algorithms for renewable energy conversion applications: A review
Observing present scarcity of fossil fuel and emissions of greenhouse gases, electricity
generated from Renewable Energy (RE) sources turns out to be the best alternative for …
generated from Renewable Energy (RE) sources turns out to be the best alternative for …
Machine learning-based approach to predict energy consumption of renewable and nonrenewable power sources
In today's world, renewable energy sources are increasingly integrated with nonrenewable
energy sources into electric grids and pose new challenges because of their intermittent and …
energy sources into electric grids and pose new challenges because of their intermittent and …
Feature selection in machine learning prediction systems for renewable energy applications
This paper focuses on feature selection problems that arise in renewable energy
applications. Feature selection is an important problem in machine learning, both in …
applications. Feature selection is an important problem in machine learning, both in …
Review on deep learning research and applications in wind and wave energy
Wind energy and wave energy are considered to have enormous potential as renewable
energy sources in the energy system to make great contributions in transitioning from fossil …
energy sources in the energy system to make great contributions in transitioning from fossil …
Cardiovascular disease detection using ensemble learning
A Alqahtani, S Alsubai, M Sha… - Computational …, 2022 - Wiley Online Library
One of the most challenging tasks for clinicians is detecting symptoms of cardiovascular
disease as earlier as possible. Many individuals worldwide die each year from …
disease as earlier as possible. Many individuals worldwide die each year from …
Forecasting solar energy production using machine learning
When it comes to large‐scale renewable energy plants, the future of solar power forecasting
is vital to their success. For reliable predictions of solar electricity generation, one must take …
is vital to their success. For reliable predictions of solar electricity generation, one must take …