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Showing 141 - 160 results of 197 for search '(( east research random tree algorithm ) OR (( fast OR face) research random three algorithm ))', query time: 0.32s Refine Results
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    Cowpea genetic diversity, population structure and genome-wide association studies in Malawi: insights for breeding programs by Michael M. Chipeta, John Kafwambira, Esnart Yohane

    Published 2025-01-01
    “…The study assessed the effects of genotype, location, and their interactions on morphological traits. The Fixed and Random Model Circulating Probability Unification (FarmCPU) algorithm was used to identify significant MTAs.ResultsThe morphological traits showed significant genotype, location, and interaction effects. …”
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    ProCAPTCHA: A profile-based CAPTCHA for personal password authentication. by Nilobon Nanglae, Pattarasinee Bhattarakosol

    Published 2024-01-01
    “…ProCAPTCHA leverages keystroke dynamics and personal information to create unique CAPTCHAs that are difficult for intruders to solve. ProCAPTCHA's algorithm generates CAPTCHA based on the user's profile data, ensuring randomness and uniqueness for each login. …”
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    Fog Service Placement Optimization: A Survey of State-of-the-Art Strategies and Techniques by Hemant Kumar Apat, Veena Goswami, Bibhudatta Sahoo, Rabindra K. Barik, Manob Jyoti Saikia

    Published 2025-03-01
    “…To solve this problem, various authors proposed different algorithms like the randomized algorithm, heuristic algorithm, meta heuristic algorithm, machine learning algorithm, and graph-based algorithm for finding the optimal placement. …”
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    Precision autofocus in optical microscopy with liquid lenses controlled by deep reinforcement learning by Jing Zhang, Yong-feng Fu, Hao Shen, Quan Liu, Li-ning Sun, Li-guo Chen

    Published 2024-12-01
    “…We explored various action group design methods and improved the microscope autofocus speed to an average of 3.15 time steps. Additionally, parallel “state” dataset lists with random sampling training are proposed which enhances the model’s adaptability to unknown samples, thereby improving its generalization capability. …”
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    Detection of kidney bean leaf spot disease based on a hybrid deep learning model by Yiwei Wang, Qianyu Wang, Yue Su, Binghan Jing, Meichen Feng

    Published 2025-04-01
    “…Based on this dataset, a novel hybrid deep learning model framework is proposed, which integrates deep learning models (EfficientNet-B7, MobileNetV3, ResNet50, and VGG16) for feature extraction with machine learning algorithms (Logistic Regression, Random Forest, AdaBoost, and Stochastic Gradient Boosting) for classification. …”
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