Showing 61 - 80 results of 572 for search 'T54 (classification)', query time: 0.07s Refine Results
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    Optimizing Deep Learning Acceleration on FPGA for Real-Time and Resource-Efficient Image Classification by Ahmad Mouri Zadeh Khaki, Ahyoung Choi

    Published 2025-01-01
    “…Our implementation achieves high classification accuracy, with Top-1 accuracy of 89.54% and 87.47% for VGG16 and VGG19, respectively, while delivering significant reductions in inference latency (7.29× and 6.6× compared to CPU-based alternatives). …”
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    Evaluation of antibiotic consumption using WHO -antimicrobial consumption tool and AWaRe classification by Julie Birdie Wahlang, Reuben P. Syiem, Chayna Sarkar, Nari M. Lyngdoh, Iadarilang Tiewsoh, Dhriti K. Brahma, Aitilutmon Surong, Arky J Langstieh

    Published 2024-12-01
    “…Results: The results of the study showed that female patients (54%), made up a significant majority of those receiving antimicrobial treatment. …”
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    Predictive value of myositis antibodies: role of semiquantitative classification and positivity for more than one autoantibody by Anne M Kerola, Arno Hänninen, Annukka Pietikäinen, Julia Barantseva, Annaleena Pajander

    Published 2025-01-01
    “…We extracted clinical diagnoses from the Care Register for Health Care between January 2013 and June 2022.Results The PPV for a myositis diagnosis (ever during data collection) was highest for anti-HMGCR antibodies (94%), followed by anti-MDA5, anti-Jo-1 and anti-TIF1-γ (49–54%). Regarding other myositis antibodies, 18–42% of cases had myositis. …”
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    Comparison of conditioning factor classification criteria in large-scale statistically based landslide susceptibility models by M. Sinčić, S. Bernat Gazibara, M. Rossi, S. Mihalić Arbanas

    Published 2025-01-01
    “…Therefore, the paper focuses on the crucial step of classifying continuous landslide conditioning factors for susceptibility modelling by presenting an innovative comprehensive analysis that resulted in 54 landslide susceptibility models to test 11 classification criteria (scenarios which vary from stretched values, partially stretched classes, heuristic approach, classification based on studentized contrast and landslide presence, and commonly used classification criteria, such as natural neighbour, quantiles and geometrical intervals) in combination with 5 statistical methods. …”
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    Design of a mammography X-ray image classification assistant system adapted to Chinese population by SUN Changjin, TONG Fei, WU Yi

    Published 2025-01-01
    “…Objective‍ ‍To construct a mammography image classification assistant system suitable for Chinese population, and explore the potential of artificial intelligence technology to assist early screening of breast cancer in China. …”
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