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  1. 9221

    A Comparative Study between Different Machine Learning Algorithms for Estimating the Vehicular Delay at Signalized Intersections by Yazan Alatoom, Abdallah Al-Hamdan

    Published 2025-01-01
    “…Consequently, this study aimed to compare a wide array of machine learning algorithms, including Artificial Neural Networks (ANN), Random Forest (RF), decision tree, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), AdaBoost, Gradient Boost, XGBoost, and Partial Least Squares (PLS) regression. …”
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  2. 9222

    GCS-YOLO: A Lightweight Detection Algorithm for Grape Leaf Diseases Based on Improved YOLOv8 by Qiang Hu, Yunhua Zhang

    Published 2025-04-01
    “…In view of the issues of high complexity, significant computational resource consumption, and slow inference speed in the detection algorithm for grape leaf diseases, this paper proposes GCS-YOLO, a lightweight detection algorithm based on an improved YOLOv8. …”
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  3. 9223

    An efficient algorithm for pedestrian fall detection in various image degradation scenarios based on YOLOv8n by Jianhui Xun, Xuefeng Wang, Xiufang Wang, Xiaoliang Fan, Peishuai Yang, Zhifei Zhang

    Published 2025-03-01
    “…To address these issues, this paper proposes an enhanced pedestrian fall detection algorithm called Pedestrian Chain-of-Thought Prompted Adaptive Enhancer YOLO (PCE-YOLO), based on YOLOv8n. …”
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    Photovoltaic fault detection algorithm using ensemble learning enhanced with deep neural network feature engineering by Maryam Parvin, Hossein Yousefi, Behnam Mohammadi-Ivatloo

    Published 2025-09-01
    “…The developed novel algorithm demonstrated transformative potential, particularly when integrated with EL strategies. …”
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    Article
  16. 9236

    Learning Gaussian graphical models from correlated data by Zeyuan Song, Zeyuan Song, Sophia Gunn, Stefano Monti, Stefano Monti, Gina M. Peloso, Ching-Ti Liu, Kathryn Lunetta, Paola Sebastiani, Paola Sebastiani, Paola Sebastiani

    Published 2025-07-01
    “…Gaussian Graphical Models (GGMs) are a type of network modeling that uses partial correlation rather than correlation for representing complex relationships among multiple variables. …”
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    Comparison of principal component analysis algorithms for imputation in agrometeorological data in high dimension and reduced sample size. by Valter Cesar de Souza, Sergio Augusto Rodrigues, Luís Roberto Almeida Gabriel Filho

    Published 2024-01-01
    “…The aim of this study was to evaluate the performance of alternative multivariate procedures for principal component analysis (PCA), using the Nonlinear Iterative Partial Least Squares (NIPALS) and Expectation-Maximization (EM) algorithms, for imputing missing data in time series of meteorological variables. …”
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