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Showing 1 - 8 results of 8 for search '(( binary risk codon optimization algorithm ) OR ( binary based single estimation algorithm ))*', query time: 0.46s Refine Results
  1. 1
    Multicategory Angle-Based Learning for Estimating Optimal Dynamic Treatment Regimes With Censored Data

    Multicategory Angle-Based Learning for Estimating Optimal Dynamic Treatment Regimes With Censored Data by Fei Xue (24567)

    Published 2021
    “…Specifically, the proposed method obtains the optimal DTR via integrating estimations of decision rules at multiple stages into a single multicategory classification algorithm without imposing additional constraints, which is also more computationally efficient and robust. …”
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  2. 2
    State-of-the-art methods for X-ray image classification. Summarised in terms of the classifier, preprocessing, and features extraction used and their performance using the different datasets. CLAHE: Contrast limited adaptive histogram equalization. DT: Decision Tree, HOG: Histogram of Oriented Gradients, WMF: Weighted Median Filtering, LSTM: Long short-term memory. PWLGBP: Weighted Local Gabor Binary Pattern. ENNSA: Ensemble Neural Net Sentinel Algorithm. IGLCM: Insistent Grey Level Co-occurrence Matrix. DF-GAN: Deep Fusion Generative Adversarial Networks. The performance metrics are the True Positive Rate, recall or Sensitivity (TPR), the True Negative Rate, Negative Recall, or Specificity (TNR), and the Accuracy Rate (ACC).

    State-of-the-art methods for X-ray image classification. Summarised in terms of the classifier, preprocessing, and features extraction used and their performance using the differen... by Antonio Quintero-Rincón (21087716)

    Published 2025
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  3. 3
    Rule-Based PCT Overview.

    Rule-Based PCT Overview. by Prateek Gupta (3174555)

    Published 2023
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  4. 4
    Data_Sheet_2_Predicting Axial Length From Choroidal Thickness on Optical Coherence Tomography Images With Machine Learning Based Algorithms.docx

    Data_Sheet_2_Predicting Axial Length From Choroidal Thickness on Optical Coherence Tomography Images With Machine Learning Based Algorithms.docx by Hao-Chun Lu (12965318)

    Published 2022
    “…</p>Methods<p>The CT was estimated from 3 points of each image. We used five machine-learning base algorithms to construct the classifiers. …”
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  5. 5
    Data_Sheet_1_Predicting Axial Length From Choroidal Thickness on Optical Coherence Tomography Images With Machine Learning Based Algorithms.docx

    Data_Sheet_1_Predicting Axial Length From Choroidal Thickness on Optical Coherence Tomography Images With Machine Learning Based Algorithms.docx by Hao-Chun Lu (12965318)

    Published 2022
    “…</p>Methods<p>The CT was estimated from 3 points of each image. We used five machine-learning base algorithms to construct the classifiers. …”
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  6. 6
    Community Detection with Heterogeneous Block Covariance Model

    Community Detection with Heterogeneous Block Covariance Model by Xiang Li (114679)

    Published 2025
    “…<p>Community detection is the task of clustering objects based on their pairwise relationships. Most of the model-based community detection methods, such as the stochastic block model and its variants, are designed for networks with binary (yes/no) edges. …”
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  7. 7
    Demonstration data on the set up of consumer wearable device for exposure and health monitoring in population studies

    Demonstration data on the set up of consumer wearable device for exposure and health monitoring in population studies by Antonis Michanikou (8996667)

    Published 2022
    “…The implementation of this GPS data filling algorithm allowed replacing the missing 5-minute intervals with estimated values. …”
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  8. 8
    Raw LC-MS/MS and RNA-Seq Mitochondria data

    Raw LC-MS/MS and RNA-Seq Mitochondria data by Stefano Martellucci (16284377)

    Published 2025
    “…The results were subsequently processed to filter out common contaminants, decoy hits from the reverse database, and protein groups identified by a single peptide. The data were filtered as follows: (a) binary expression of a protein (i.e., protein exclusively identified in either scLRP1+/+ or scLRP1-/-) was only considered relevant if all scLRP1+/+ samples or all scLRP1-/- samples expressed the protein. …”
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