预兆好 发表于 2025-3-27 00:03:38
http://reply.papertrans.cn/83/8231/823091/823091_31.png挖掘 发表于 2025-3-27 01:30:49
Conference proceedings 2022ligent and contextual systems, natural language processing, network systems and applications, computational imaging and vision, decision support and control systems, and data modeling and processing for industry 4.0.诱拐 发表于 2025-3-27 07:58:06
http://reply.papertrans.cn/83/8231/823091/823091_33.png震惊 发表于 2025-3-27 10:00:55
http://reply.papertrans.cn/83/8231/823091/823091_34.png一大块 发表于 2025-3-27 17:19:21
http://reply.papertrans.cn/83/8231/823091/823091_35.pngfleeting 发表于 2025-3-27 18:56:32
http://reply.papertrans.cn/83/8231/823091/823091_36.png祸害隐伏 发表于 2025-3-28 00:33:13
,Error Investigation of Pre-trained BERTology Models on Vietnamese Natural Language Inference,nchmark dataset affect the performance of the pre-trained BETology-based models. In addition, the data parameters of ViNLI are also measured and analyzed on the accuracy of these models to see if it has any impact on the accuracy of the model.冰河期 发表于 2025-3-28 06:00:18
Predicting Metastasis-Free Survival Using Clinical Data in Non-small Cell Lung Cancer,dex = 0.63 for a model with three clinical covariates. In addition, we created also a nomogram that could be applied to predicting the probability of metastases in newly diagnosed patients. In conclusion, solely based on clinical data, it is possible to predict the time to metastasis.人类 发表于 2025-3-28 09:28:02
,G-Fake: Tell Me How It is Shared and I Shall Tell You If It is Fake, the trustworthiness of users. In fact, G-Fake does not even require access to the underlying social graph, nor to the interactions between users. Our experimental evaluation conducted on real-world data shows that G-Fake can limit the spread of fake news in the earliest stages of propagation with an accuracy of 96.8%.covert 发表于 2025-3-28 10:48:39
Shapley Additive Explanations for Text Classification and Sentiment Analysis of Internet Movie Datagative or positive labels. Our sentiment analysis model is evaluated on the Internet Movie Database (IMDB) datasets which have rich vocabulary and coherence of the textual data. Results showed that the model predicted 89% of the user reviews correctly. This model is very flexible for extending it to the unlabeled data.