Showing 1 - 20 results of 782 for search '(( learning ((e decrease) OR (we decrease)) ) OR ( ai ((large decrease) OR (marked decrease)) ))', query time: 0.66s Refine Results
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    Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx by Rasita Vinay (21006911)

    Published 2025
    “…Introduction<p>The emergence of artificial intelligence (AI) large language models (LLMs), which can produce text that closely resembles human-written content, presents both opportunities and risks. …”
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    Overview of the WeARTolerance program. by Ana Beato (20489933)

    Published 2024
    “…The quantitative results from Phase 1 demonstrated a decreasing trend in all primary outcomes. In phase 2, participants acknowledged the activities’ relevance, reported overall satisfaction with the program, and showed great enthusiasm and willingness to learn more. …”
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    Feasibility of AI-powered assessment scoring: Can large language models replace human raters? by Michael Jaworski III (22156096)

    Published 2025
    “…After ChatGPT-4.5 was publicly released, reliability decreased notably (e.g. ICC = −0.046 for BVMT-R Trial 3), and average scoring discrepancies per test increased (e.g. …”
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    Using Environmental Mixture Exposure-Triggered Biological Knowledge-Driven Machine Learning to Predict Early Pregnancy Loss by Mengyuan Ren (14724676)

    Published 2025
    “…The GO-integrated model, with an area under the curve (AUC) of 0.876, outperformed others (AUC = 0.819), even when the sample size decreased to 60% of the total. Additionally, this framework deciphered critical exposures (e.g., serum selenium and chromium) and biological perturbations (e.g., cell population proliferation and apoptotic nuclear changes), linking mixture exposure to EPL. …”
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    Passive sensing data. by Thierry Jean (20691795)

    Published 2025
    “…The CrossCheck dataset includes 6,364 mental state surveys using 4-point ordinal rating scales and 23,551 days of smartphone sensor data contributed by patients with schizophrenia. We trained 120 machine learning models to forecast 10 mental states (e.g., Calm, Depressed, Seeing things) from passive sensor data on 2 predictive tasks (ordinal regression, binary classification) with 2 learning algorithms (XGBoost, LSTM) over 3 forecast horizons (same day, next day, next week). …”
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    Surveys. by Thierry Jean (20691795)

    Published 2025
    “…The CrossCheck dataset includes 6,364 mental state surveys using 4-point ordinal rating scales and 23,551 days of smartphone sensor data contributed by patients with schizophrenia. We trained 120 machine learning models to forecast 10 mental states (e.g., Calm, Depressed, Seeing things) from passive sensor data on 2 predictive tasks (ordinal regression, binary classification) with 2 learning algorithms (XGBoost, LSTM) over 3 forecast horizons (same day, next day, next week). …”
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