Результаты поиска по 'elimination tree':
Найдено статей: 3
  1. Editor’s note
    Computer Research and Modeling, 2024, v. 16, no. 7, pp. 1533-1538
  2. Shaheen L., Rasheed B., Mazzara M.
    Tree species detection using hyperspectral and Lidar data: A novel self-supervised learning approach
    Computer Research and Modeling, 2024, v. 16, no. 7, pp. 1747-1763

    Accurate tree identification is essential for ecological monitoring, biodiversity assessment, and forest management. Traditional manual survey methods are labor-intensive and ineffective over large areas. Advances in remote sensing technologies including lidar and hyperspectral imaging improve automated, exact detection in many fields.

    Nevertheless, these technologies typically require extensive labeled data and manual feature engineering, which restrict scalability. This research proposes a new method of Self-Supervised Learning (SSL) with the SimCLR framework to enhance the classification of tree species using unlabelled data. SSL model automatically discovers strong features by merging the spectral data from hyperspectral data with the structural data from LiDAR, eliminating the need for manual intervention.

    We evaluate the performance of the SSL model against traditional classifiers, including Random Forest (RF), Support Vector Machines (SVM), and Supervised Learning methods, using a dataset from the ECODSE competition, which comprises both labeled and unlabeled samples of tree species in Florida’s Ordway-Swisher Biological Station. The SSL method has been demonstrated to be significantly more effective than traditional methods, with a validation accuracy of 97.5% compared to 95.56% for Semi-SSL and 95.03% for CNN in Supervised Learning.

    Subsampling experiments showed that the SSL technique is still effective with less labeled data, with the model achieving good accuracy even with only 20% labeled data points. This conclusion demonstrates SSL’s practical applications in circumstances with insufficient labeled data, such as large-scale forest monitoring.

    Keywords: self-supervised learning, tree species detection, SimCLR, hyperspectral imagery, lidar data.
  3. Lemtyuzhnikova D.V.
    Parallel representation of local elimination algorithm for accelerating the solving sparse discrete optimization problems
    Computer Research and Modeling, 2015, v. 7, no. 3, pp. 699-705

    The decomposition algorithms provide approaches to deal with NP-hardness in solving discrete optimization problems (DOPs). In this article one of the promising ways to exploit sparse matrices — local elimination algorithm in parallel interpretation (LEAP) are demonstrated. That is a graph-based structural decomposition algorithm, which allows to compute a solution in stages such that each of them uses results from previous stages. At the same time LEAP heavily depends on elimination ordering which actually provides solving stages. Also paper considers tree- and block-parallel for LEAP and required realization process of it comparison of a several heuristics for obtaining a better elimination order and shows how is related graph structure, elimination ordering and solving time.

    Keywords: discrete optimization, volunteer computing, local elimination algorithm, parallel computing, sparse problems, elimination tree.
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