Search alternatives:
models optimization » model optimization (Expand Search), process optimization (Expand Search), wolf optimization (Expand Search)
image models » climate models (Expand Search), change models (Expand Search), scale models (Expand Search)
models optimization » model optimization (Expand Search), process optimization (Expand Search), wolf optimization (Expand Search)
image models » climate models (Expand Search), change models (Expand Search), scale models (Expand Search)
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A* Path-Finding Algorithm to Determine Cell Connections
Published 2025“…To address this, the research integrates a modified A* pathfinding algorithm with a U-Net convolutional neural network, a custom statistical binary classification method, and a personalized Min-Max connectivity threshold to automate the detection of astrocyte connectivity.…”
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ROC curve for binary classification.
Published 2024“…<div><p>This study aims to develop an optimally performing convolutional neural network to classify Alzheimer’s disease into mild cognitive impairment, normal controls, or Alzheimer’s disease classes using a magnetic resonance imaging dataset. …”
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Confusion matrix for binary classification.
Published 2024“…<div><p>This study aims to develop an optimally performing convolutional neural network to classify Alzheimer’s disease into mild cognitive impairment, normal controls, or Alzheimer’s disease classes using a magnetic resonance imaging dataset. …”
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Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm
Published 2025“…In this work, we propose a novel framework that integrates </p><p dir="ltr">Convolutional Neural Networks (CNNs) for image classification and a binary Grey Wolf Optimization (GWO) </p><p dir="ltr">algorithm for feature selection. …”
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Summary of existing CNN models.
Published 2024“…<div><p>This study aims to develop an optimally performing convolutional neural network to classify Alzheimer’s disease into mild cognitive impairment, normal controls, or Alzheimer’s disease classes using a magnetic resonance imaging dataset. …”
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Data_Sheet_1_Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM).pdf
Published 2024“…A support vector machine (SVM) is attractive because binary classification can be represented as an optimization problem, in particular as a Quadratic Unconstrained Binary Optimization (QUBO) model, which, in turn, maps naturally to an Ising model, thereby making annealing—classical, quantum, and hybrid—an attractive approach to explore. …”
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Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment
Published 2019“…The MapReduce parallel programming model on the Hadoop platform is used to perform an adaptive fusion of hue, local binary pattern (LBP) and scale-invariant feature transform (SIFT) features extracted from images to derive optimal combinations of weights. …”
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Testing results for classifying AD, MCI and NC.
Published 2024“…<div><p>This study aims to develop an optimally performing convolutional neural network to classify Alzheimer’s disease into mild cognitive impairment, normal controls, or Alzheimer’s disease classes using a magnetic resonance imaging dataset. …”
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Flowchart scheme of the ML-based model.
Published 2024“…<b>I)</b> Testing data consisting of 20% of the entire dataset. <b>J)</b> Optimization of hyperparameter tuning. <b>K)</b> Algorithm selection from all models. …”
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