Showing 961 - 980 results of 28,660 for search 'Classification three', query time: 0.24s Refine Results
  1. 961

    Using the excitation/inhibition ratio to optimize the classification of autism and schizophrenia by Lavinia Carmen Uscătescu, Christopher J. Hyatt, Jack Dunn, Martin Kronbichler, Vince Calhoun, Silvia Corbera, Kevin Pelphrey, Brian Pittman, Godfrey Pearlson, Michal Assaf

    Published 2025-07-01
    “…Classification performance using H only was higher in the exploratory dataset (AUC = 84%) compared to the replication dataset (AUC = 72%). …”
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  2. 962

    Neural network-based classification and regression of magnetohydrodynamic modes in tokamaks by L. Bardoczi, K. Won, N.J. Richner, A.C. Brown, D. Chow, P. Li, J. Monahan

    Published 2025-01-01
    “…We present a machine learning-based magnetohydrodynamic (MHD) classifier and regressor that utilizes real or complex-valued 3D magnetic sensor array data to determine neoclassical tearing mode (NTM) onset times in tokamaks with millisecond accuracy. …”
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  3. 963

    Artificial Intelligence-Based Classification of Anatomical Sites in Esophagogastroduodenoscopy Images by Yuan P, Ma ZH, Yan Y, Li SJ, Wang J, Wu Q

    Published 2024-12-01
    “…Primary outcomes included diagnostic accuracy, sensitivity, and specificity.Results: A total of 160,308 images from 27 categories of the upper endoscopy anatomy classification were included in this retrospective research. …”
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  4. 964

    Fracability evaluation and classification of deep coal reservoirs in the Shenfu block by Wenchun PENG, Honggang MI, Lifu XU, Jian WU

    Published 2025-03-01
    “…The evaluation method of deep coal reservoir fracability is established to provide a basis and theoretical guidance for reservoir classification and transformation and the efficient development of deep coalbed methane in the study area.…”
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  5. 965

    Modeling and Classification of the Behavioral Patterns of Students Participating in Online Examination by B. J. Ferdosi, M. Rahman, A. M. Sakib, T. Helaly

    Published 2023-01-01
    “…The proposed system has five major parts: (1) identification and coordinate extraction of selected facial landmarks using MediaPipe; (2) orientation classification of the head, eye, and lips with K-NN classifier, based on the landmarks; (3) identification of abnormal movements; (4) calculation of a cheating score based on abnormal movement patterns; and (5) a visual representation of students’ behavior to support the proctor for early intervention. …”
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