urinary-tract 发表于 2025-3-21 20:03:20
书目名称Knowledge Graphs and Semantic Web影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0543942<br><br> <br><br>书目名称Knowledge Graphs and Semantic Web影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0543942<br><br> <br><br>书目名称Knowledge Graphs and Semantic Web网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0543942<br><br> <br><br>书目名称Knowledge Graphs and Semantic Web网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0543942<br><br> <br><br>书目名称Knowledge Graphs and Semantic Web被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0543942<br><br> <br><br>书目名称Knowledge Graphs and Semantic Web被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0543942<br><br> <br><br>书目名称Knowledge Graphs and Semantic Web年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0543942<br><br> <br><br>书目名称Knowledge Graphs and Semantic Web年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0543942<br><br> <br><br>书目名称Knowledge Graphs and Semantic Web读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0543942<br><br> <br><br>书目名称Knowledge Graphs and Semantic Web读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0543942<br><br> <br><br>geriatrician 发表于 2025-3-21 22:02:04
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Malware Detection Using Machine Learning,tection for the same, we present a machine-learning based technique for predicting Windows PE files as benign or malignant based on fifty-seven of their attributes. We have used the Brazilian Malware dataset, which had around 1,00,000 samples and 57 labels. We have made seven models, and have achiev捕鲸鱼叉 发表于 2025-3-22 15:50:56
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Relation Classification: How Well Do Neural Network Approaches Work?,pre-defined set of abstract relation labels. A benchmark data set for this task is the SemEval-2010 Task 8 data set. Neural network approaches are currently the methods that give state-of-art results on a wide range of NLP problems. There is also the claim that the models trained on one task carry o背景 发表于 2025-3-23 04:28:49
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An Ontological Model for the Failure Detection in Power Electric Systems,ific aspects of the electricity grid and do not consider the necessary elements for the representation of a fault detection system based on Smart Grid trends. In this paper, we describe the development process of EPFDO, an ontology composed of a network of ontologies that allow the representation of