Showing 4,301 - 4,320 results of 5,488 for search 'decision three algorithm', query time: 0.13s Refine Results
  1. 4301

    Concrete Crack Detection and Segregation: A Feature Fusion, Crack Isolation, and Explainable AI-Based Approach by Reshma Ahmed Swarna, Muhammad Minoar Hossain, Mst. Rokeya Khatun, Mohammad Motiur Rahman, Arslan Munir

    Published 2024-08-01
    “…Builders and engineers often face difficulties with image resolution, detecting fine cracks, and differentiating between structural and non-structural issues. Enhanced algorithms and analysis techniques are needed for more accurate assessments. …”
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    Article
  2. 4302

    Developing a Prototype Machine Learning Model to Predict Quality of Life Measures in People Living With HIV by Mercadal-Orfila G, Serrano López de las Hazas J, Riera-Jaume M, Herrera-Perez S

    Published 2025-01-01
    “…Moderate performance was observed for the P3CEQ (Adj. R² = 0.753) and TSQM (Adj. R² = 0.698), reflecting variability in model accuracy across instruments. …”
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    Mammography-based artificial intelligence for breast cancer detection, diagnosis, and BI-RADS categorization using multi-view and multi-level convolutional neural networks by Hongna Tan, Qingxia Wu, Yaping Wu, Bingjie Zheng, Bo Wang, Yan Chen, Lijuan Du, Jing Zhou, Fangfang Fu, Huihui Guo, Cong Fu, Lun Ma, Pei Dong, Zhong Xue, Dinggang Shen, Meiyun Wang

    Published 2025-05-01
    “…The study consisted of three parts: (1) evaluation of AIS performance in malignancy diagnosis; (2) stratified analysis of BI-RADS 3–4 subgroups with AIS; and (3) reassessment of BI-RADS 0 breasts with AIS assistance. …”
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    Article
  6. 4306

    Prediction of EGFR mutations in non-small cell lung cancer: a nomogram based on 18F-FDG PET and thin-section CT radiomics with machine learning by Jianbo Li, Qin Shi, Yi Yang, Jikui Xie, Qiang Xie, Ming Ni, Xuemei Wang, Xuemei Wang

    Published 2025-04-01
    “…After selecting optimal radiomic features, four machine learning algorithms, including logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), were used to develop and validate radiomics models. …”
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    Article
  7. 4307

    A VVC intra coding method based on fast partition for coding unit by ZHONG Hui, LU Yu, YIN Haibing, HUANG Xiaofeng

    Published 2024-08-01
    “…Experimental results show that the proposed method can significantly reduce encoding complexity, with a 74.3% reduction in encoding time compared to the original encoder with only 3.3% rate loss. …”
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    Melanoma risk prediction models by Nikolić Jelena, Lončar-Turukalo Tatjana, Sladojević Srđan, Marinković Marija, Janjić Zlata

    Published 2014-01-01
    “…A continuous melanoma database growth would provide for further adjustments and enhancements in model accuracy as well as offering a possibility for successful application of more advanced data mining algorithms.…”
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    Noninvasive imaging biomarker reveals invisible microscopic variation in acute ischaemic stroke (≤ 24 h): a multicentre retrospective study by Kui Sun, Rongchao Shi, Xinxin Yu, Ying Wang, Wei Zhang, Xiaoxia Yang, Mei Zhang, Jian Wang, Shu Jiang, Haiou Li, Bing Kang, Tong Li, Shuying Zhao, Yu Ai, Jianfeng Qiu, Haiyan Wang, Ximing Wang

    Published 2025-01-01
    “…Patients in five institutions (n = 592) were combined to generate training and internal validation sets, remaining in three institutions as external validation sets (n = 204, 53 and 273). …”
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  12. 4312

    Machine learning models for discriminating clinically significant from clinically insignificant prostate cancer using bi-parametric magnetic resonance imaging by Hakan Ayyıldız, Okan İnce, Esin Korkut, Merve Gülbiz Dağoğlu Kartal, Atadan Tunacı, Şükrü Mehmet Ertürk

    Published 2025-07-01
    “…Patients with a Gleason score of 3+3 were considered to have clinically ciPCa, and patients with a Gleason score of 3+4 and above were considered to have csPCa. …”
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