Showing 3,001 - 3,020 results of 8,285 for search '(( significant decrease decrease ) OR ( significance levels decrease ))~', query time: 0.33s Refine Results
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    Trends in ESR per survey round by age group. by Alex Daama (18351052)

    Published 2024
    “…Targeted efforts for counseling focusing on married men, men who had multiple sex partners, and men with lower levels of education may decrease ESR.</p></div>…”
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    Multiscale QM/MM Simulations Identify the Roles of Asp239 and 1‑OH···Nucleophile in Transition State Stabilization in Arabidopsis thaliana Cell-Wall Invertase 1 by Wijitra Jitonnom (22410515)

    Published 2025
    “…However, details at the atomic level about the role of neighboring residues and enzyme-substrate interactions during catalysis are not fully understood. …”
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    Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML by Ayush Garg (21090944)

    Published 2025
    “…The important findings of our studies are as follows: (i) there is no effect of threshold optimization on ranking metrics such as AUC and AUPR, but AUC and AUPR get affected by class-weighting and SMOTTomek; (ii) for ML methods RF and SVM, significant percentage improvement up to 375, 33.33, and 450 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy, which are suitable for performance evaluation of imbalanced data sets; (iii) for AutoML libraries AutoGluon-Tabular and H2O AutoML, significant percentage improvement up to 383.33, 37.25, and 533.33 over all the data sets can be achieved, respectively, for F1 score, MCC, and balanced accuracy; (iv) the general pattern of percentage improvement in balanced accuracy is that the percentage improvement increases when the class ratio is systematically decreased from 0.5 to 0.1; in the case of F1 score and MCC, maximum improvement is achieved at the class ratio of 0.3; (v) for both ML and AutoML with balancing, it is observed that any individual class-balancing technique does not outperform all other methods on a significantly higher number of data sets based on F1 score; (vi) the three external balancing techniques combined outperformed the internal balancing methods of the ML and AutoML; (vii) AutoML tools perform as good as the ML models and in some cases perform even better for handling imbalanced classification when applied with imbalance handling techniques. …”
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    Baseline characteristics. by Daniela Sánchez-Santiesteban (21192342)

    Published 2025
    “…In Colombia, despite a healthcare system covering 97% of the population, socioeconomic disparities persist. Lower income levels are associated with decreased survival, potentially due to delays in diagnosis or treatment and a higher probability of advanced staging at diagnosis, These inequities persist even among relatively advantaged populations, such as formal employee who are assumed to have fewer barriers to accessing healthcare services compared to informal workers.…”
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