Showing 1 - 20 results of 285 for search '(( binary image model optimization algorithm ) OR ( primary data were optimization algorithm ))*', query time: 0.76s Refine Results
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    Features selected by optimization algorithms. by Afnan M. Alhassan (18349378)

    Published 2024
    “…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”
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    ROC curve for binary classification. by Nicodemus Songose Awarayi (18414494)

    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. by Nicodemus Songose Awarayi (18414494)

    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 by Max Weng (22327159)

    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 by Hussein Ali Bardan (21976208)

    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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    Hybrid feature selection algorithm of CSCO-ROA. by Afnan M. Alhassan (18349378)

    Published 2024
    “…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”
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    Summary of existing CNN models. by Nicodemus Songose Awarayi (18414494)

    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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    S1 Data - by Guangwei Liu (181992)

    Published 2023
    “…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”
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    Data_Sheet_1_Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM).pdf by Sai Sakunthala Guddanti (17739363)

    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. …”