Showing 121 - 140 results of 433 for search 'T36 (classification)', query time: 0.05s Refine Results
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    Identification of the optimal candidates to benefit from surgery and chemotherapy among elderly female breast cancer patients with bone metastases by Yuchen Hu, Junfeng Tang, Xiaofeng Liu, Yusheng Sun, Baojun Gong, Qing Gao

    Published 2025-02-01
    “…The nomogram’s AUC for forecasting OS at 12, 24, and 36 months was 0.753, 0.748, and 0.745 in the training cohort, and 0.744, 0.723, and 0.723 in the validation cohort. …”
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  5. 125

    Diagnosis of Cognitive and Mental Disorders: A New Approach Based on Spectral–Spatiotemporal Analysis and Local Graph Structures of Electroencephalogram Signals by Arezoo Sanati Fahandari, Sara Moshiryan, Ateke Goshvarpour

    Published 2025-01-01
    “…Background/Objectives: The classification of psychological disorders has gained significant importance due to recent advancements in signal processing techniques. …”
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    Compressing fully connected layers of deep neural networks using permuted features by Dara Nagaraju, Nitin Chandrachoodan

    Published 2023-07-01
    “…The authors also showed that the proposed method can be used in the classification stage of the transfer learning networks.…”
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    Gambaran Tekanan Darah pada Pasien Sindrom Koroner Akut di RS Khusus Jantung Sumatera Barat Tahun 2011-2012 by Meidiza Ariandiny, Afriwardi ., Masrul Syafri

    Published 2014-05-01
    “…Based on the age classification of hypertension, found that 46-55 years were 30%, 66-75 years were 25%, 56-65 years were 24%, > 76 years were 10%, 36-45 years were 0.8%, and < 35 years were 0.2%. based on gender classification of hypertension found that male gender were 74% and women were 26%. …”
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    A deep learning based model for diabetic retinopathy grading by Samia Akhtar, Shabib Aftab, Oualid Ali, Munir Ahmad, Muhammad Adnan Khan, Sagheer Abbas, Taher M. Ghazal

    Published 2025-01-01
    “…In our research, we have developed a deep neural network named RSG-Net (Retinopathy Severity Grading) to classify DR into 4 stages (multi-class classification) and 2 stages (binary classification). …”
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