Showing 221 - 240 results of 1,436 for search '((((((mode OR model) OR (more OR more)) OR more) OR more) OR more) OR made) screening algorithm', query time: 0.26s Refine Results
  1. 221

    Blinded Validation of Breath Biomarkers of Lung Cancer, a Potential Ancillary to Chest CT Screening. by Michael Phillips, Thomas L Bauer, Renee N Cataneo, Cassie Lebauer, Mayur Mundada, Harvey I Pass, Naren Ramakrishna, William N Rom, Eric Vallières

    Published 2015-01-01
    “…Outcome modeling: We modeled the expected effects of combining breath biomarkers with chest CT on the sensitivity and specificity of lung cancer screening.…”
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    Article
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  4. 224

    Artificial intelligence for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region by S. I. Panin, V. A. Suvorov, A. V. Zubkov, S. A. Bezborodov, A. A. Panina, N. V. Kovalenko, A. R. Donsckaia, I. G. Shushkova, A. V. Bykov, Ya. A. Marenkov

    Published 2024-07-01
    “…Determination of the optimal machine learning model for the creation of software for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region. …”
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    Article
  5. 225

    A Novel Method for Screening the PMU Phase Angle Difference Data Based on Hyperplane Clustering by Ancheng Xue, Shuang Leng, Yecheng Li, Feiyang Xu, Kenneth E. Martin, Jingsong Xu

    Published 2019-01-01
    “…First, we develop the hyperplane cluster method to cluster the phase angle difference data. Second, in order to screen out the right data type, this paper compares the virtual reactance parameters of each data type obtained by voltage mean to the line reactance parameter given by the system model. …”
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  6. 226

    Efficient text-to-video retrieval via multi-modal multi-tagger derived pre-screening by Yingjia Xu, Mengxia Wu, Zixin Guo, Min Cao, Mang Ye, Jorma Laaksonen

    Published 2025-03-01
    “…In this work, we present a plug-and-play multi-modal multi-tagger-driven pre-screening framework, which pre-screens a substantial number of videos before applying any TVR algorithms, thereby efficiently reducing the search space of videos. …”
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  7. 227
  8. 228

    Preterm preeclampsia screening and prevention: a comprehensive approach to implementation in a real-world setting by Stefania Ronzoni, Shamim Rashid, Aimee Santoro, Elad Mei-Dan, Jon Barrett, Nanette Okun, Tianhua Huang

    Published 2025-01-01
    “…Abstract Background Preeclampsia significantly impacts maternal and perinatal health. Early screening using advanced models and primary prevention with low-dose acetylsalicylic acid for high-risk populations is crucial to reduce the disease’s incidence. …”
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    Article
  9. 229

    Virtual Screening of Conjugated Polymers for Organic Photovoltaic Devices Using Support Vector Machines and Ensemble Learning by Fang-Chung Chen

    Published 2019-01-01
    “…Additionally, the predictive performance could be further improved by “blending” the results of the SVM and random forest models. The resulting ensemble learning algorithm might open up a new opportunity for more precise, high-throughput virtual screening of conjugated polymers for OPV devices.…”
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  10. 230

    High-throughput screening and machine learning classification of van der Waals dielectrics for 2D nanoelectronics by Yuhui Li, Guolin Wan, Yongqian Zhu, Jingyu Yang, Yan-Fang Zhang, Jinbo Pan, Shixuan Du

    Published 2024-11-01
    “…Here, we employed a topology-scale algorithm to screen vdW materials consisting of zero-dimensional (0D), one-dimensional (1D), and 2D motifs from Materials Project database. …”
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    Article
  11. 231

    High throughput computational screening and interpretable machine learning for iodine capture of metal-organic frameworks by Haoyi Tan, Yukun Teng, Guangcun Shan

    Published 2025-05-01
    “…In addition to 6 structural features, 25 molecular features (encompassing the types of metal and ligand atoms as well as bonding modes) and 8 chemical features (including heat of adsorption and Henry’s coefficient) were incorporated to enhance the prediction accuracy of the machine learning algorithms. …”
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  12. 232

    Review of Josh Simons’ Book "Algorithms for the People – Democracy in the Age of AI" by Thomas Klikauer

    Published 2025-05-01
    “… Increasingly, artificial intelligence, algorithms and machine learning models guide what Internet users see and read on their screens. …”
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    Article
  13. 233

    Analysis of imaging differences between high-resolution CT and digital radiography chest films in pneumoconiosis screening by Lijuan LIU, Fenghong WANG

    Published 2025-03-01
    “…HRCT enables systematic observation of the evolution and progression of pneumoconiosis, providing reliable evidence for diagnosis.ObjectiveTo provide reliable evidences for the early screening of pneumoconiosis, By analyzing the imaging difference between HRCT and DR chestfilms in pneumoconiosis screening.MethodsSix casting workers in a casting forging company suspected of early stage of pneumoconiosis through regular occupational health examination screening were recruited , and 64 rows of spiral CT thin layer were scanned and reconstructed by high-resolution bone algorithm. …”
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  14. 234

    Changed definition of disease and broader screening criteria had little impact on prevalence of gestational diabetes mellitus by Lina Grønvall, Finn Egil Skjeldestad

    Published 2022-06-01
    “…Conclusions The introduction of broader screening criteria and a more liberal case definition increased the population eligible for GDM screening by 45%. …”
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    Student knowledge tracking based multi-indicator exercise recommendation algorithm by Bin ZHUGE, Zhenghu YIN, Wenxue SI, Lei YAN, Ligang DONG, Xian JIANG

    Published 2022-09-01
    “…Personalized exercise recommendation was an important topic in the era of education informatization, the forgetting laws of students in the learning process were ignored by the traditional problem recommendation algorithm, which failed to fully tap the students’ knowledge mastery level and the common characteristics of similar students, insufficient, could not reasonably promote students’ learning of new knowledge or help students find and fill omissions.In view of the above defects, a multi-index exercise recommendation method based on student knowledge tracking was proposed, which was divided into two modules: preliminary screening and re-filtering of exercises, focusing on the novelty, difficulty and diversity of exercise recommendation.Firstly, a knowledge probability prediction (SF-KCCP) model combined with students’ forgetting law was constructed to ensure the novelty of the recommended exercises.Then, students’ knowledge and concept mastery level was accurately excavated based on the dynamic key-value knowledge tracking (DKVMN) model to ensure that exercises of appropriate difficulty were recommended.Finally, the user-based collaborative filtering (UserCF) algorithm was integrated into the re-filtering module, and the similarity between student groups was used to achieve the diversity of recommendation results.The proposed method is demonstrated by extensive experiments to achieve better performance than some existing baseline models.…”
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    Article
  17. 237

    Student knowledge tracking based multi-indicator exercise recommendation algorithm by Bin ZHUGE, Zhenghu YIN, Wenxue SI, Lei YAN, Ligang DONG, Xian JIANG

    Published 2022-09-01
    “…Personalized exercise recommendation was an important topic in the era of education informatization, the forgetting laws of students in the learning process were ignored by the traditional problem recommendation algorithm, which failed to fully tap the students’ knowledge mastery level and the common characteristics of similar students, insufficient, could not reasonably promote students’ learning of new knowledge or help students find and fill omissions.In view of the above defects, a multi-index exercise recommendation method based on student knowledge tracking was proposed, which was divided into two modules: preliminary screening and re-filtering of exercises, focusing on the novelty, difficulty and diversity of exercise recommendation.Firstly, a knowledge probability prediction (SF-KCCP) model combined with students’ forgetting law was constructed to ensure the novelty of the recommended exercises.Then, students’ knowledge and concept mastery level was accurately excavated based on the dynamic key-value knowledge tracking (DKVMN) model to ensure that exercises of appropriate difficulty were recommended.Finally, the user-based collaborative filtering (UserCF) algorithm was integrated into the re-filtering module, and the similarity between student groups was used to achieve the diversity of recommendation results.The proposed method is demonstrated by extensive experiments to achieve better performance than some existing baseline models.…”
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    Article
  18. 238

    Panel defect detection algorithm based on improved Faster R-CNN by Chen Wanqin, Tang Qingshan, Huang Tao

    Published 2022-01-01
    “…Experimental results show that the accuracy and recognition rate of the optimized network model have been greatly improved.…”
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    Two-test algorithms for infectious disease diagnosis: Implications for COVID-19. by Sunil Pokharel, Lisa J White, Jilian A Sacks, Camille Escadafal, Amy Toporowski, Sahra Isse Mohammed, Solomon Chane Abera, Kekeletso Kao, Marcela De Melo Freitas, Sabine Dittrich

    Published 2022-01-01
    “…A two-test algorithm comprising a rapid screening test followed by confirmatory laboratory testing can reduce false positive rate, produce rapid results and conserve laboratory resources, but can lead to large number of missed cases in high prevalence setting. …”
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