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  1. 141

    Some Elements of Operational Modal Analysis by Rune Brincker

    Published 2014-01-01
    “…This paper gives an overview of the main components of operational modal analysis (OMA) and can serve as a tutorial for research oriented OMA applications. …”
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  2. 142

    On the Search for Supersingular Elliptic Curves and Their Applications by Ismel Martinez-Diaz, Rashad Ali, Muhammad Kamran Jamil

    Published 2025-01-01
    “…As our main result, we define for the first time an objective function to measure the supersingularity in ordinary curves, and we apply local search and a genetic algorithm using that function. …”
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  3. 143
  4. 144

    Feasibility of automating the determination of changes in forest areas using satellite images (Case Study: Central Alborz protected area) by Amir Satari Rad, Behzad Rayegani, Ali Jahani, Hamid Goshtasb Meigooni

    Published 2024-08-01
    “…Therefore, detecting changes with the help of multi-temporal data in forest levels allows us to prevent further destruction by automatically identifying these changes. The main goal of this research is to identify the thresholds and apply them to the NDVI vegetation index images in MODIS sensors and automatic monitoring of forest areas.Materials and MethodsThis research was conducted in the Central Alborz protected area with an area of more than 398 thousand hectares and very rich vegetation with more than 1100 plant species. …”
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  8. 148

    Old Drugs, New Indications (Review) by I. I. Miroshnichenko, E. A. Valdman, I. I. Kuz'min

    Published 2023-02-01
    “…Machine learning (ML) algorithms: Bayes classifier, logistic regression, support vector machine, decision tree, random forest and others are successfully used in biochemical pharmaceutical, toxicological research. …”
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  9. 149

    Methodology for Estimating the Cost of Construction Equipment Based on the Analysis of Important Characteristics Using Machine Learning Methods by Nataliya Boyko, Oleksii Lukash

    Published 2023-01-01
    “…The study built and analyzed models using machine learning methods (linear and polynomial regression, decision trees, random forest, support vector machine, and neural network). …”
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  10. 150
  11. 151

    Human Clustering Based on Graph Embedding and Space Functions of Trajectory Stay Points on Campus by Ke Xie, Tao Wang, Pan Zhong, Zihao Zhao, Zixiang Wang

    Published 2025-03-01
    “…The graph embedding algorithm is used to calculate feature vector representations of nodes in the network, which can capture complex relationships among nodes through biased random walks. …”
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  15. 155

    Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China by Baoxin Zhao, Jingzhong Zhu, Youbiao Hu, Qimeng Liu, Yu Liu

    Published 2022-01-01
    “…The main purpose of this research is to apply the logistic regression (LR) model, the support vector machine (SVM) model based on radial basis function, the random forest (RF) model, and the coupled model of the whale optimization algorithm (WOA) and genetic algorithm (GA) with RF, to make landslide susceptibility mapping for the Ankang City of Shaanxi Province, China. …”
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  16. 156

    Impact of Sanctions on Industry Indices by E. A. Fedorova, A. R. Nevredinov

    Published 2024-12-01
    “…All data were obtained for the period from 01.01.2014 to 31.12.2023. The research methodology is based on mathematical modeling using the BERTopic topic modeling algorithm. …”
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  17. 157
  18. 158

    Application Analysis of Credit Scoring of Financial Institutions Based on Machine Learning Model by Yi Wu, Yuwen Pan

    Published 2021-01-01
    “…Personal credit evaluation based on big data is one of the hot research topics. This paper mainly completes three works. …”
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  19. 159
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    Comparative Analysis of Diabetes Prediction Models Using the Pima Indian Diabetes Database by Zhao Yize

    Published 2025-01-01
    “…The K-means model operates by grouping data points into separate clusters according to their characteristics, achieving an accuracy of 90.04% in diabetes prediction. In comparison, the random forest model, which builds multiple decision trees (DT) to do their predictions, demonstrates superior performance over several widely used algorithms such as K-Nearest Neighbours (KNN), Logistic Regression (LR), DT, Support Vector Machines (SVM), and Gradient Boosting (GB). …”
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