Affectation
发表于 2025-3-30 08:45:28
Human Factors Psychology in Surgerynships. In the experiment, we conducted the tests with various Indonesian pre-trained BERT models to enhance the performance of multi-aspect extraction on Indonesian hotel reviews. Our findings indicate that . pre-trained model can improve the classifier performance and achieve an impressive F1-scor
摇摆
发表于 2025-3-30 16:16:08
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思考
发表于 2025-3-30 18:49:58
Purchasing, Sales and LogisticsUGE score is computed for the generated summaries against the reference summaries to analyse the performance of the model. Our results show that GPT-3.5 performs slightly better in summarizing scientific articles as compared to news articles with an average ROUGE score of 0.35 and 0.31, respectively
capsaicin
发表于 2025-3-30 21:12:24
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Defraud
发表于 2025-3-31 01:11:33
Limit Performances and Queuing Effects, FaceNet, and ST-GCN, to deliver targeted outcomes. Our evaluation of URSA’s performance, conducted with video footage from bustling areas like college campuses and railway stations, underscores its exceptional accuracy in handling challenging real-world scenarios.
执拗
发表于 2025-3-31 05:51:03
https://doi.org/10.1007/978-3-540-36874-8image utilized. A total of 530 website images were collected with 272 fraud website images and 258 fraud website images. The websites are partitioned into 80% (370 images) samples as training set, 10% (80 images) samples as testing set, and the rest 10% (80 images) samples as validation set. Three C
saphenous-vein
发表于 2025-3-31 11:00:08
The Classical Number Domains Z, Q, R, and Ction of ransomware and compared the performance of artificial neural networks (ANN) and deep neural networks (DNN) in terms of accurately classifying ransomware and goodware. The suggested framework secured an accuracy of 98.56% with ANNs, and achieved a slightly better performance (99.06%) when ANN
和音
发表于 2025-3-31 17:06:22
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食草
发表于 2025-3-31 20:24:01
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Robust
发表于 2025-3-31 23:45:04
https://doi.org/10.1007/b138337, and text by merging and complementing aspects traditionally handled by humans with those typically handled by deep learning. During the study, three popular multimodal emotion recognition datasets, IEMOCAP, CMU-MOSI, and CMU-MOSEI, are analyzed and ranked based on their quality. This study will he