Titlebook: Artificial Intelligence Applications and Innovations; 14th IFIP WG 12.5 In Lazaros Iliadis,Ilias Maglogiannis,Vassilis Plagia Conference pr

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書目名稱Artificial Intelligence Applications and Innovations影響因子(影響力)




書目名稱Artificial Intelligence Applications and Innovations影響因子(影響力)學(xué)科排名




書目名稱Artificial Intelligence Applications and Innovations網(wǎng)絡(luò)公開度




書目名稱Artificial Intelligence Applications and Innovations網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Artificial Intelligence Applications and Innovations被引頻次




書目名稱Artificial Intelligence Applications and Innovations被引頻次學(xué)科排名




書目名稱Artificial Intelligence Applications and Innovations年度引用




書目名稱Artificial Intelligence Applications and Innovations年度引用學(xué)科排名




書目名稱Artificial Intelligence Applications and Innovations讀者反饋




書目名稱Artificial Intelligence Applications and Innovations讀者反饋學(xué)科排名

倔強一點

Finding Influential Users in Twitter Using Cluster-Based Fusion Methods of Result Listsal networks. The novelty of our approach lies in the fact that we incorporate a set of features for characterizing social media authors, including both nodal and topical metrics, along with new features concerning temporal aspects of user participation on the topic. We also take advantage of cluster

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青少年

Spam Filtering in Social Networks Using Regularized Deep Neural Networks with Ensemble Learningdeveloped to deal with this complex problem. Traditional machine learning approaches such as neural networks, support vector machine and Na?ve Bayes classifiers are not effective enough to process and utilize complex features present in high-dimensional data on social network spam. To overcome this

豐滿中國

Sub-event Detection on Twitter Networketection problem using three statistical methods: Kalman Filter, Gaussian Process, and Probabilistic Principal Component Analysis. These methods are used to construct the probability distribution of percentage change in the number of tweets. Outliers are identified as future observations that do not

說笑

laparoscopy

神秘

Keywords-To-Text Synthesis Using Recurrent Neural Networkch tagging library is employed to extract verbs and nouns from the texts used in our work, a part of which are then considered, after automatic eliminations, as the aforementioned keywords. Our ultimate aim is to train a Recurrent Neural Network to map the keyword sequence of a text to the entire te

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魯莽