Showing 801 - 820 results of 2,970 for search 'interpretive (structural OR structure) modeling', query time: 0.19s Refine Results
  1. 801

    Complex concept of rudoneftegasogenesis (on the example of the Caspian, Crimean and Black sea regions and Arctic) by V. M. Kharchenko, D. V. Lapta

    Published 2022-07-01
    “…Discussions and conclusion: the basis of the work is the interpretation of the CST, the construction of a geological-tectonic and fluid-dynamic model. …”
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    Article
  2. 802

    Soft Classification in a Composite Source Model by Yuefeng Cao, Jiakun Liu, Wenyi Zhang

    Published 2025-06-01
    “…A composite source model consists of an intrinsic state and an extrinsic observation. …”
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    Article
  3. 803
  4. 804

    Deep learning model for gastrointestinal polyp segmentation by Zitong Wang, Zeyi Wang, Pengyu Sun

    Published 2025-05-01
    “…Our method employs an encoder-decoder structure with a pre-trained ConvNeXt model as the encoder to learn multi-scale feature representations. …”
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  5. 805

    A model for measuring the Quality of Work-Life by R. Forouzandeh Joonaghani, I Raeesi Vanani, S. A. Hosseini

    Published 2025-04-01
    “…Additionally, it is found that Organizational - Physical Conditions (SM = 0.300, p < 0.05), Organizational Work-Life Balance (SM = 0.281, p < 0.05), Social aspect - Communication with Coworkers (SM = 0.291, p < 0.05), and social aspect - Communication with Managers (SM = 0.209, p < 0.05) significantly affect Organizational Psychological Atmosphere and indirect effect on Quality of Work-Life.CONCLUSION: Findings from the Partial Least Squares - Structural Equation Modeling analysis reveal no direct effect of physical conditions, job factors, or communication with managers on the Quality of Work-Life, although indirect relationships were supported. …”
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    Article
  6. 806
  7. 807

    Deep Learning-Based Prediction of Pitch Response for Floating Offshore Wind Turbines by Ruifeng Chen, Ke Zhang, Min Luo, Ye An, Lixiang Guo

    Published 2024-12-01
    “…Moreover, the Shapley additive explanations (SHAP) interpretation is utilized to reveal the most significant features influencing structural responses. …”
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  8. 808
  9. 809

    Geoelectrical Tomography Investigating and Modeling of Fractures Network around Bittit Spring (Middle Atlas, Morocco) by Kh. Qarqori, M. Rouai, F. Moreau, G. Saracco, O. Dauteuil, D. Hermitte, M. Boualoul, C. Le Carlier de Veslud

    Published 2012-01-01
    “…Joint interpretation of geophysical, geological, structural, and synthetic simulation data allowed identifying a conductive horizontal shallow layer overlying two subvertical families of fractures of NE-SW and NW-SE directions. …”
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  10. 810

    On Minimizing Influences Under Multi-Attribute Models by Bo-Yao Wang

    Published 2025-06-01
    “…The proposed framework enables structured and interpretable evaluation of influence in complex cooperative systems with heterogeneous participation and conflicting objectives.…”
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  11. 811

    XGBoost algorithm optimized by simulated annealing genetic algrithm for permeability prediction modeling of carbonate reservoirs by Changbing Huang, Xinyu Zhu, Mingyu Lu, Yuling Zhang, Shengbo Yang

    Published 2025-04-01
    “…Abstract Carbonate reservoir has strong heterogeneity, complex pore structure and poor correlation between porosity and permeability, so the traditional permeability model can not meet the needs of logging interpretation. …”
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  12. 812
  13. 813

    Monte Carlo optimization based QSAR modeling of the cytotoxicity of acrylic acid-based dental monomers by Bošković Mirjana, Stanković Saša, Živković Jelena V., Veselinović Aleksandar M.

    Published 2025-01-01
    “…Given that the majority of molecular databases adhere to this molecular structure conformation, the featured QSAR models can serve as a rapid and precise screening tool for novel dental monomers.…”
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  14. 814

    Research on Personalized Course Resource Recommendation Method Based on GEMRec by Enliang Wang, Zhixin Sun

    Published 2025-01-01
    “…Innovatively, GEMRec introduces graph edit distance into the recommendation system to measure the structural similarity between a learner’s knowledge state and course content at the knowledge graph level. …”
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  15. 815

    Diagnostic Analysis of Crosswalk Safety Hazards in Pedestrian Environments: A SHAP-Enhanced Machine Learning Approach With Street-View Imagery by Caryl Anne M. Barquilla, Jeongwoo Lee

    Published 2025-01-01
    “…These results highlight the importance of combining visual and structural data for thorough risk assessment and further the use of interpretable machine learning in urban safety research. …”
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  16. 816

    Temporal Markers as a Way of Modeling the Artistic Space of the Poem “Pe aceeași ulicioară” by M. Eminescu by A. V. Diehl

    Published 2023-02-01
    “…The purpose of the study is to identify the specifics of the temporal structure of the poem “Pe aceeași ulicioară”, taking into account the meaning-forming function of temporal markers in its cognitive scenario, structuring the temporal perspective of the poem and modeling the emotional world of the lyrical hero.Methodology and sources. …”
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  17. 817

    Explaining drivers of housing prices with nonlinear hedonic regressions by Heng Wan, Pranab K. Roy Chowdhury, Jim Yoon, Parin Bhaduri, Vivek Srikrishnan, David Judi, Brent Daniel

    Published 2025-09-01
    “…For instance, while the linear model indicates a steady housing price increase over time, our interpretable ML model detects a post-2008 decline, with smaller properties experiencing the sharpest drop.…”
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  18. 818

    DIA-TSK: A Dynamic Incremental Adaptive Takagi–Sugeno–Kang Fuzzy Classifier by Hao Chen, Chenhui Sha, Mingqing Jiao, Changbin Shao, Shang Gao, Hualong Yu, Bin Qin

    Published 2025-03-01
    “…To solve these issues, this study proposes a novel training method consisting of a single dynamic classifier—named the dynamic incremental adaptive Takagi–Sugeno–Kang fuzzy classifier (DIA-TSK)—which leverages the superior non-linear modeling capabilities and interpretability of the TSK fuzzy system. …”
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  19. 819

    Comparing factor mixture modeling and conditional Gaussian mixture variational autoencoders for cognitive profile clustering by Matteo Orsoni, Sara Giovagnoli, Sara Garofalo, Noemi Mazzoni, Matilde Spinoso, Mariagrazia Benassi

    Published 2025-05-01
    “…While traditional methods like factor mixture modeling (FMM) have proven robust for identifying latent cognitive structures, recent advancements in deep learning may offer the potential to capture more intricate and complex cognitive patterns.MethodsThis study compares FMM (specifically, FMM-1 and FMM-2 models using age as a covariate) with a Conditional Gaussian Mixture Variational Autoencoder (CGMVAE). …”
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  20. 820

    Perceptual-Preference-Based Touring Routes in Xishu Gardens Using Panoramic Digital-Twin Modeling by Xueqian Gong, Zhanyuan Zhu, Li Guo, Yong Zhong, Deshun Zhang, Jing Li, Manqin Yao, Wei Yong, Mengjia Li, Yujie Huang

    Published 2025-04-01
    “…Xishu Gardens, an exemplary narrative of classical Chinese gardens, faces challenges in preserving its commemorative spatial structures while accommodating modern visitors’ needs. …”
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