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Showing 181 - 200 results of 233 for search '(( main research random tree algorithm ) OR (( fast OR face) research random three algorithm ))', query time: 0.12s Refine Results
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    Hyperspectral estimation of chlorophyll density in winter wheat using fractional-order derivative combined with machine learning by Chenbo Yang, Chenbo Yang, Meichen Feng, Juan Bai, Hui Sun, Rutian Bi, Lifang Song, Chao Wang, Yu Zhao, Wude Yang, Lujie Xiao, Meijun Zhang, Xiaoyan Song

    Published 2025-01-01
    “…Hyperspectral monitoring models for winter wheat ChD were constructed using 8 machine learning algorithms, including partial least squares regression, support vector regression, multi-layer perceptron regression, random forest regression, extra-trees regression (ETsR), decision tree regression, K-nearest neighbors regression, and gaussian process regression, based on the full spectrum band and the band selected by competitive adaptive reweighted sampling (CARS). …”
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  4. 184

    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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  7. 187

    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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    AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector by Şenda Yıldırım, Ahmet Deniz Yücekaya, Mustafa Hekimoğlu, Meltem Ucal, Mehmet Nafiz Aydin, İrem Kalafat

    Published 2025-06-01
    “…Additionally, this predictive approach supports workforce planning and scheduling within after-sales service centers, aligning with AI-driven labor optimization frameworks such as those explored in the AI4LABOUR project. Four algorithms in machine learning—Decision Tree, Random Forest, LightGBM (LGBM), and Extreme Gradient Boosting (XGBoost)—were assessed for their forecasting capabilities. …”
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  19. 199

    Google Earth Engine-based Mangrove Mapping and Change Detections for Sustainable Development in Tien Yen District, Quang Ninh Province, Vietnam by M. H. Nguyen, N. T. Nguyen, G. Y. I. Ryadi, M. V. Nguyen, T. L. Duong, C.-H. Lin, T. B. Nguyen

    Published 2024-11-01
    “…Four supervised classification algorithms, including Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes classifier, and Classification and Regression Trees (CART) have been implemented on GEE platform to select the best algorithm to produce spatial-temporal mangrove maps, then change detection of mangroves is performed. …”
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