Search alternatives:
learning algorithm » learning algorithms (Expand Search)
methods algorithm » means algorithm (Expand Search), network algorithm (Expand Search)
method algorithm » network algorithm (Expand Search), means algorithm (Expand Search), mean algorithm (Expand Search)
elements method » element method (Expand Search)
data learning » meta learning (Expand Search), deep learning (Expand Search), a learning (Expand Search)
based methods » mixed methods (Expand Search)
learning algorithm » learning algorithms (Expand Search)
methods algorithm » means algorithm (Expand Search), network algorithm (Expand Search)
method algorithm » network algorithm (Expand Search), means algorithm (Expand Search), mean algorithm (Expand Search)
elements method » element method (Expand Search)
data learning » meta learning (Expand Search), deep learning (Expand Search), a learning (Expand Search)
based methods » mixed methods (Expand Search)
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Types of machine learning algorithms.
Published 2024“…Thus, the objectives of this study are to develop an appropriate model for predicting the risk of undernutrition and identify its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms.</p><p>Materials and methods</p><p>This study used the latest nationally representative cross-sectional Bangladesh demographic health survey (BDHS), 2017–18 data. …”
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Decision tree algorithms.
Published 2025“…<div><p>Imbalanced intrusion classification is a complex and challenging task as there are few number of instances/intrusions generally considered as minority instances/intrusions in the imbalanced intrusion datasets. Data sampling methods such as over-sampling and under-sampling methods are commonly applied for dealing with imbalanced intrusion data. …”
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Algorithmic experimental parameter design.
Published 2024“…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”
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List of the time used by each algorithm.
Published 2024“…To this end, this paper proposes an entropy-based dynamic ensemble classification algorithm (EDAC) to consider data streams with class imbalance and concept drift simultaneously. …”