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• W3123902186 abstract "Presently a day's human relations are kept up by online life systems. Customary connections now days are outdated. To keep up in affiliation, sharing thoughts, trade information between we utilize web-based social networking organizing locales. Web based life organizing locales like Twitter, Facebook, LinkedIn and so forth are accessible in the correspondence condition. Through Twitter media clients share their sentiments, interests, information to others by messages. Simultaneously a portion of the client's mislead the certifiable clients. These certified clients are additionally called requested clients and the clients what misguidance's identity is called spammers. These spammers present undesirable data on the non-spam clients. The non-spammers may retweet them to other people and they follow the spammers. Generally most of the spam messages are in the form of text, images and different multimedia formats. Considering all different formats in one process may not give the best classification results. In this paper address the process and classification of text spam messages. Classification of text messages is a complex task in order to achieve this deep learning based hybrid VAE-CNN and LSTM model is proposed and evaluated the model using the performance metrics of precision, recall and F measure metrics." @default.
• W3123902186 created "2021-02-01" @default.
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• W3123902186 date "2020-12-31" @default.
• W3123902186 modified "2023-09-23" @default.
• W3123902186 title "Classification of Social Media Text Spam Using VAE-CNN and LSTM Model" @default.
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• W3123902186 doi "https://doi.org/10.18280/isi.250605" @default.
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