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Showing 1 - 20 results of 138 for search '(( learning ai decrease ) OR ( ct ((largest decrease) OR (marked decrease)) ))', query time: 0.46s Refine Results
  1. 1
    Boxplot comparing Ct values for HD in flies and clothes.

    Boxplot comparing Ct values for HD in flies and clothes. by Philippe Ndzomo (17706039)

    Published 2025
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  2. 2
    AI driven topic clustering and trend analysis workflow.

    AI driven topic clustering and trend analysis workflow. by Min Ho An (7894658)

    Published 2025
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  3. 3
    Overview of the study workflow for AI-driven musculoskeletal ultrasound analysis.

    Overview of the study workflow for AI-driven musculoskeletal ultrasound analysis. by M. Mendez (6624953)

    Published 2025
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  4. 4
    Overview of the study workflow for AI-driven musculoskeletal ultrasound analysis.

    Overview of the study workflow for AI-driven musculoskeletal ultrasound analysis. by M. Mendez (6624953)

    Published 2025
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  5. 5
    S1 Data. Bibliometric analysis. S2 Data. Estrogen level analysis. S3 Data. MicroCT quantification. S4 Data. Markers of bone metabolism.

    S1 Data. Bibliometric analysis. S2 Data. Estrogen level analysis. S3 Data. MicroCT quantification. S4 Data. Markers of bone metabolism. by Yan She (8639796)

    Published 2025
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  6. 6
    Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx

    Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx by Rasita Vinay (21006911)

    Published 2025
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  7. 7
    Embracing GenAI in Engineering Education: Lessons from the Trenches

    Embracing GenAI in Engineering Education: Lessons from the Trenches by Lorena A. Barba (97553)

    Published 2025
    “…Students quickly developed patterns of using AI as a shortcut rather than a learning companion, leading to decreased attendance and an "illusion of competence." …”
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  8. 8
    Skin marks is the S1 Fig title.

    Skin marks is the S1 Fig title. by Mercedes Soto-González (20422078)

    Published 2024
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  9. 9
    Inherently interpretable deep learning framework highlights clinically relevant image regions.

    Inherently interpretable deep learning framework highlights clinically relevant image regions. by Kerol Djoumessi (21349992)

    Published 2025
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  10. 10
    Clinical diagnostic efficacy comparison of the proposed hybrid model and benchmark machine learning models. Metrics are reported as point estimates with 95% CIs. This table offers a discussion of the measures of high interest in clinical implementation. Sensitivity (true positive rate) reflects the ability to correctly identify patients with CVD, while Specificity (true negative rate) indicates the ability to correctly rule out patients without CVD. The Negative Likelihood Ratio quantifies how much the odds of having the disease decrease with a negative test result, where smaller values indicate stronger diagnostic power. The suggested model has the best balance of the greatest sensitivity and specificity, as well as the least Negative Likelihood Ratio, which highlights its strength and better performance as a diagnostic tool in the clinical practice.

    Clinical diagnostic efficacy comparison of the proposed hybrid model and benchmark machine learning models. Metrics are reported as point estimates with 95% CIs. This table offers... by Medhat A. Tawfeek (22522087)

    Published 2025
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  11. 11
    Graphical representation of the optimization process in machine learning, focusing on the adjustment of weights during gradient descent.

    Graphical representation of the optimization process in machine learning, focusing on the adjustment of weights during gradient descent. by Liu Liu (512237)

    Published 2025
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  12. 12
    Supplemental information for "Data Driven Model to Estimate Furrow Irrigation Sediment Loss in Western U.S."

    Supplemental information for "Data Driven Model to Estimate Furrow Irrigation Sediment Loss in Western U.S." by Bradley King (16921360)

    Published 2025
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  13. 13
    Supplementary file 1_Harnessing AI for aphasia: a case report on ChatGPT's role in supporting written expression.docx

    Supplementary file 1_Harnessing AI for aphasia: a case report on ChatGPT's role in supporting written expression.docx by Avery K. Allen (21449492)

    Published 2025
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  14. 14
    Presentation 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip

    Presentation 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

    Published 2025
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  15. 15
    Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip

    Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

    Published 2025
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  16. 16
    Architecture of Multi-Layer U-Net Model.

    Architecture of Multi-Layer U-Net Model. by Muhammad Zubair (728141)

    Published 2025
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  17. 17
    GM Mask image count after preprocessing.

    GM Mask image count after preprocessing. by Muhammad Zubair (728141)

    Published 2025
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  18. 18
    Proposed Multi-Layer U-Net model for gray matter segmentation.

    Proposed Multi-Layer U-Net model for gray matter segmentation. by Muhammad Zubair (728141)

    Published 2025
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  19. 19
    Gray Matter segmentation results using the proposed Multi-layer U-Net Model.

    Gray Matter segmentation results using the proposed Multi-layer U-Net Model. by Muhammad Zubair (728141)

    Published 2025
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  20. 20
    Gray matter segmentation results using the Attention U-Net model.

    Gray matter segmentation results using the Attention U-Net model. by Muhammad Zubair (728141)

    Published 2025
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