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A Multi-Classifier Based Prediction Model for Phishing Emails Detection Using Topic Modelling, Named Entity Recognition and Image Processing

机译：基于多分类器的网络钓鱼电子邮件检测预测模型，使用主题建模，命名实体识别和图像处理

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C. Emilin Shyni; S. Sarju; S. Swamynathan;
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年度 2016
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1. A Multi-Classifier Based Prediction Model for Phishing Emails Detection Using Topic Modelling, Named Entity Recognition and Image Processing [J] . C. Emilin Shyni, S. Sarju, S. Swamynathan Circuits and Systems . 2016,第9期

机译：基于主题识别，命名实体识别和图像处理的基于多分类器的网络钓鱼电子邮件检测预测模型
2. A Multi-Classifier Based Prediction Model for Phishing Emails Detection Using Topic Modelling, Named Entity Recognition and Image Processing [J] . C. Emilin Shyni, S. Sarju, S. Swamynathan Circuits and systems . 2016,第9期

机译：基于主题识别，命名实体识别和图像处理的基于多分类器的网络钓鱼电子邮件预测模型
3. Event identification in web social media through named entity recognition and topic modeling [J] . Konstantinos N. Vavliakis, Andreas L. Symeonidis, Pericles A. Mitkas Data & Knowledge Engineering . 2013,第nova期

机译：通过命名实体识别和主题建模在网络社交媒体中进行事件识别
4. Phishing Email Detection based on Named Entity Recognition [C] . Vit Listik, Simon Let, Jan Sedivy, International Conference on Information Systems Security and Privacy . 2019

机译：基于命名实体识别的网络钓鱼电子邮件检测
5. Topics in the interpretation of seismic data: I. Earthquake seismogram interpretation for the classroom. II. Gas saturation prediction and effect of low frequencies on acoustic impedance images at Foinaven Field. III. Physical reasonability and accuracy of spheroidal porosity models for seismic velocity prediction. [D] . Wagner, Sean R. 2005

机译：地震数据解释的主题：I.教室地震图解释。二。 Foinaven场的气体饱和度预测和低频对声阻抗图像的影响。三，球形孔隙度模型在地震速度预测中的物理合理性和准确性。
6. Semi-Supervised Bidirectional Long Short-Term Memory and Conditional Random Fields Model for Named-Entity Recognition Using Embeddings from Language Models Representations [O] . Min Zhang, Guohua Geng, Jing Chen 2020

机译：使用语言模型表示的嵌入式识别命名实体识别的半监控双向短期内存和条件随机字段模型
7. Phishing Email Detection based on Entity Recognition [O] . Let Šimon 100

机译：基于实体识别的网络钓鱼邮件检测

A Multi-Classifier Based Prediction Model for Phishing Emails Detection Using Topic Modelling, Named Entity Recognition and Image Processing

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