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Titlebook: Intelligent Computing Theories and Application; 17th International C De-Shuang Huang,Kang-Hyun Jo,Prashan Premaratne Conference proceedings

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樓主: COAX
31#
發(fā)表于 2025-3-27 00:12:34 | 只看該作者
Social Media Adverse Drug Reaction Detection Based on Bi-LSTM with Multi-head Attention Mechanisme lack of space relationship between features in the process of classification and building the model of the problem of low efficiency. Experimental results on SMM4H corpus validate that the proposed method is effective and has good performance in the detection of ADR events in social media. To impr
32#
發(fā)表于 2025-3-27 02:21:30 | 只看該作者
33#
發(fā)表于 2025-3-27 07:32:53 | 只看該作者
Inversion of k-Nearest Neighbours Algorithm for Extracting SNPs Discriminating Human Populationsh means that these SNPs are noises for classification. The rest SNPs are important for classification. We validate our method on HapMap data, IKNN has a better performance than neural network algorithm and KNN algorithm.
34#
發(fā)表于 2025-3-27 10:25:55 | 只看該作者
ComPAT: A Comprehensive Pathway Analysis Tools71 interactive relationships. All pathways in ComPAT were converted into graphs. ComPAT provides comprehensive annotation for pathways, including network visualization and topology information, gene subcellular localization and gene–gene interaction strength. What‘s more, ComPAT integrated five type
35#
發(fā)表于 2025-3-27 14:24:49 | 只看該作者
Review of Methods for Data Collection Experiments with People with Dementia and the Impact of COVID-e efficacy and safety of each of the methods. It is concluded that the choice of method to be utilized in future data collection experiments is heavily dependent on the type and severity of the dementia the participants are experiencing, and that the choice of remote or COVID-secure methods should b
36#
發(fā)表于 2025-3-27 21:31:02 | 只看該作者
KGRN: Knowledge Graph Relational Path Network for Target Prediction of TCM Prescriptionslation between the prescriptions and the targets. Moreover, by emphasizing the KG relations when aggregating neighborhood information, the Relational Path Network of the KGRN model has the ability to capture the relationship dependence in the KG path, which enhances the ability to predict the relati
37#
發(fā)表于 2025-3-28 01:15:56 | 只看該作者
38#
發(fā)表于 2025-3-28 03:58:11 | 只看該作者
39#
發(fā)表于 2025-3-28 09:57:59 | 只看該作者
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/i/image/469488.jpg
40#
發(fā)表于 2025-3-28 12:35:06 | 只看該作者
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