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Titlebook: Recent Challenges in Intelligent Information and Database Systems; 14th Asian Conferenc Edward Szczerbicki,Krystian Wojtkiewicz,Marek Krót

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樓主: Considerate
31#
發(fā)表于 2025-3-27 00:03:38 | 只看該作者
32#
發(fā)表于 2025-3-27 01:30:49 | 只看該作者
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.
33#
發(fā)表于 2025-3-27 07:58:06 | 只看該作者
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發(fā)表于 2025-3-27 10:00:55 | 只看該作者
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發(fā)表于 2025-3-27 17:19:21 | 只看該作者
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37#
發(fā)表于 2025-3-28 00:33:13 | 只看該作者
,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.
38#
發(fā)表于 2025-3-28 06:00:18 | 只看該作者
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.
39#
發(fā)表于 2025-3-28 09:28:02 | 只看該作者
,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%.
40#
發(fā)表于 2025-3-28 10:48:39 | 只看該作者
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.
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