Showing 121 - 128 results of 128 for search '(( binary based process classification algorithm ) OR ( binary b model optimization algorithm ))', query time: 0.34s Refine Results
  1. 121

    Deactivated Cas9-Engineered Magnetic Micromotors toward a Point-of-Care Digital Viral RNA Assay by Younseong Song (9967421)

    Published 2025
    “…A convolutional neural network classification-based multiobject tracking algorithm, CNN-MOT, accurately measures the change in micromotor motion, facilitating the binary digital assay format (“1” or “0”) for simplified result interpretation without user bias. …”
  2. 122

    Deactivated Cas9-Engineered Magnetic Micromotors toward a Point-of-Care Digital Viral RNA Assay by Younseong Song (9967421)

    Published 2025
    “…A convolutional neural network classification-based multiobject tracking algorithm, CNN-MOT, accurately measures the change in micromotor motion, facilitating the binary digital assay format (“1” or “0”) for simplified result interpretation without user bias. …”
  3. 123

    Deactivated Cas9-Engineered Magnetic Micromotors toward a Point-of-Care Digital Viral RNA Assay by Younseong Song (9967421)

    Published 2025
    “…A convolutional neural network classification-based multiobject tracking algorithm, CNN-MOT, accurately measures the change in micromotor motion, facilitating the binary digital assay format (“1” or “0”) for simplified result interpretation without user bias. …”
  4. 124

    Data_Sheet_1_Combined Intrinsic Local Functional Connectivity With Multivariate Pattern Analysis to Identify Depressed Essential Tremor.docx by Xueyan Zhang (277449)

    Published 2022
    “…We aimed to combine multivariate pattern analysis (MVPA) with local brain functional connectivity to identify depressed ET.</p>Methods<p>Based on individual voxel-level local brain functional connectivity (regional homogeneity, ReHo) mapping from 41 depressed ET, 43 non-depressed ET, and 45 healthy controls (HCs), the binary support vector machine (BSVM) and multiclass Gaussian Process Classification (MGPC) algorithms were used to identify depressed ET patients from non-depressed ET and HCs, the accuracy and permutations test were used to assess the classification performance.…”
  5. 125

    Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

    Published 2025
    “…</p><p dir="ltr">Encoding: Categorical variables such as surface coating and cell type were grouped into logical classes and label-encoded to enable model compatibility.</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”
  6. 126

    Data_Sheet_1_Identifying Depressed Essential Tremor Using Resting-State Voxel-Wise Global Brain Connectivity: A Multivariate Pattern Analysis.pdf by Yufen Li (428110)

    Published 2021
    “…Recently, machine learning multivariate pattern analysis (MVPA) combined with connectivity mapping of resting-state fMRI has provided a promising way to identify patients with depressed ET at the individual level and help to reveal the brain network pathogenesis of depression in patients with ET.</p><p>Methods: Based on global brain connectivity (GBC) mapping from 41 depressed ET, 49 non-depressed ET, 45 primary depression, and 43 healthy controls (HCs), multiclass Gaussian process classification (GPC) and binary support vector machine (SVM) algorithms were used to identify patients with depressed ET from non-depressed ET, primary depression, and HCs, and the accuracy and permutation tests were used to assess the classification performance.…”
  7. 127

    Predicting childhood obesity using electronic health records and publicly available data by Robert Hammond (3631525)

    Published 2019
    “…</p><p>Methods and findings</p><p>We trained a variety of machine learning algorithms to perform both binary classification and regression. …”
  8. 128

    Table 1_Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.docx by Ali Nabavi (21097424)

    Published 2025
    “…Multiple machine learning algorithms, including Random Forest, XGBoost, Gradient Boosting, Logistic Regression, CatBoost, and MLP, were optimized and evaluated. …”