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models optimization » model optimization (Expand Search), process optimization (Expand Search), wolf optimization (Expand Search)
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image models » climate models (Expand Search), change models (Expand Search), scale models (Expand Search)
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ROC curve for binary classification.
Published 2024“…To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. …”
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Confusion matrix for binary classification.
Published 2024“…To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. …”
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A* Path-Finding Algorithm to Determine Cell Connections
Published 2025“…Future work aims to generalize this algorithm for broader biological applications by training additional Cellpose models and adapting the A* framework.…”
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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“…To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. …”
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Generalized Tensor Decomposition With Features on Multiple Modes
Published 2021“…An efficient alternating optimization algorithm with provable spectral initialization is further developed. …”
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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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Testing results for classifying AD, MCI and NC.
Published 2024“…To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. …”
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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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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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Thesis-RAMIS-Figs_Slides
Published 2024“…Importantly, this strategy locates samples adaptively on the transition between facies which improves the performance of conventional \emph{<i>MPS</i>} algorithms. In conclusion, this work shows that preferential sampling can contribute in \emph{<i>MPS</i>} even at very small sampling regimes and, as a corollary, demonstrates that prior models (obtained form a training image) can be used effectively not only to simulate non-sensed variables of the field, but to decide where to measure next.…”
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