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Titlebook: Innovative Computing; Proceedings of the 5 Yan Pei,Jia-Wei Chang,Jason C. Hung Conference proceedings 2022 The Editor(s) (if applicable) an

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樓主: nourish
31#
發(fā)表于 2025-3-26 23:44:48 | 只看該作者
Analysis for Sequential Frame with Facial Emotion Recognition Based on CNN and LSTMal network (CNN) and long short-term memory (LSTM) are combined. We extract sequential images of facial expressions from the video and input them into the CNN model individually. To solve the problem of insufficient training data, the model learns emotion-related knowledge by transfer learning on th
32#
發(fā)表于 2025-3-27 04:53:15 | 只看該作者
33#
發(fā)表于 2025-3-27 08:30:41 | 只看該作者
34#
發(fā)表于 2025-3-27 10:42:02 | 只看該作者
A Deep Learning-Based Approach for?Mammographic Architectural Distortion Classificationams among the masses and microcalcification. Physically identifying architectural distortion for radiologists is problematic because of its subtle appearance on the dense breast. Automatic early identification of breast cancer using computer algorithms from a mammogram may assist doctors in eliminat
35#
發(fā)表于 2025-3-27 15:34:11 | 只看該作者
36#
發(fā)表于 2025-3-27 20:02:29 | 只看該作者
Promoting Foreign Electronic Commerce and?Economic Welfarelibrium model to investigate how production and import taxes affect the e-commerce industry and the economy as a whole. We found that the welfare of Korea is reduced the most when import tax is imposed on both international trade margins and international transport margins. More specifically, in the
37#
發(fā)表于 2025-3-27 23:09:14 | 只看該作者
38#
發(fā)表于 2025-3-28 03:37:48 | 只看該作者
A Feature Fusion-Based Approach for?Mammographic Mass Classification Using Deep Learningcer. The manual detection of breast masses using texture analysis from digital mammograms is hard because of its diverse patterns. Automatic detection of breast masses from mammograms with computer algorithms at early phases could help physicians to avoid unnecessary biopsies. In the current study,
39#
發(fā)表于 2025-3-28 09:54:26 | 只看該作者
Recognition of?Chinese Medical Named Entity Using Multi-word Segmentation Methodn. Medical named entity recognition can transform the free text in an electronic medical record from information to data, so it has high research value and application value. However, most of the current deep learning methods use character-level segmentation for semantic feature extraction, which le
40#
發(fā)表于 2025-3-28 10:39:17 | 只看該作者
Chinese Electronic Medical Record Retrieval Method Using Fine-Tuned RoBERTa and?Hybrid Features records can not only offer great help to clinical decision-making but also bring benefits and convenience to case-based patient research and the unearthing of similar patient groups. However, the existing electronic medical record retrieval model cannot accurately and efficiently retrieve similar m
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