性上瘾 发表于 2025-3-26 23:44:48

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

lobster 发表于 2025-3-27 04:53:15

http://reply.papertrans.cn/47/4674/467344/467344_32.png

火海 发表于 2025-3-27 08:30:41

http://reply.papertrans.cn/47/4674/467344/467344_33.png

admission 发表于 2025-3-27 10:42:02

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

drusen 发表于 2025-3-27 15:34:11

http://reply.papertrans.cn/47/4674/467344/467344_35.png

cauda-equina 发表于 2025-3-27 20:02:29

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

解开 发表于 2025-3-27 23:09:14

http://reply.papertrans.cn/47/4674/467344/467344_37.png

Etymology 发表于 2025-3-28 03:37:48

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,

UNT 发表于 2025-3-28 09:54:26

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

逢迎白雪 发表于 2025-3-28 10:39:17

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
页: 1 2 3 [4] 5 6 7
查看完整版本: Titlebook: Innovative Computing; Proceedings of the 5 Yan Pei,Jia-Wei Chang,Jason C. Hung Conference proceedings 2022 The Editor(s) (if applicable) an