A Method of Combining Hidden Markov Model and Convolutional Neural Network for the 5G RCS Message Filtering
As one of the 5G applications, rich communication suite (RCS), known as the next generation of Short Message Service (SMS), contains multimedia and interactive information for a better user experience. Meanwhile, the RCS industry worries that spammers may migrate their spamming misdeeds to RCS messa...
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MDPI AG
2021-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/14/6350 |
| _version_ | 1797527680513474560 |
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| author | Bibu Gao Wenqiang Zhang |
| author_facet | Bibu Gao Wenqiang Zhang |
| author_sort | Bibu Gao |
| collection | DOAJ |
| description | As one of the 5G applications, rich communication suite (RCS), known as the next generation of Short Message Service (SMS), contains multimedia and interactive information for a better user experience. Meanwhile, the RCS industry worries that spammers may migrate their spamming misdeeds to RCS messages, the complexity of which challenges the filtering technology because each of them contains hundreds of fields with various types of data, such as texts, images and videos. Among the data, the hundreds of fields of text data contain the main content, which is adequate and more efficient for combating spam. This paper first discusses the text fields, which possibly contain spam information, then use the hidden Markov model (HMM) to weight the fields and finally use convolutional neural network (CNN) to classify the RCS messages. In the HMM step, the text fields are treated differently. The short texts of these fields are represented as feature weight sequences extracted by a feature extraction algorithm based on a probability density function. Then, the proposed HMM learns the weight sequence and produces a proper weight for each short text. Other text fields with fewer words are also weighted by the feature extraction algorithm. In the CNN step, all these feature weights first construct the RCS message matrix. The matrices of the training RCS messages are used as the CNN model inputs for learning and the matrices of testing messages are used as the trained CNN model inputs for RCS message property prediction. Four optimization technologies are introduced into the CNN classification process. Promising experiment results are achieved on the real industrial data. |
| first_indexed | 2024-03-10T09:47:13Z |
| format | Article |
| id | doaj.art-adce82d08e90447faf00670840db9e91 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:47:13Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-adce82d08e90447faf00670840db9e912023-11-22T03:08:15ZengMDPI AGApplied Sciences2076-34172021-07-011114635010.3390/app11146350A Method of Combining Hidden Markov Model and Convolutional Neural Network for the 5G RCS Message FilteringBibu Gao0Wenqiang Zhang1Academy for Engineering and Technology, Fudan University, Shanghai 200433, ChinaAcademy for Engineering and Technology, Fudan University, Shanghai 200433, ChinaAs one of the 5G applications, rich communication suite (RCS), known as the next generation of Short Message Service (SMS), contains multimedia and interactive information for a better user experience. Meanwhile, the RCS industry worries that spammers may migrate their spamming misdeeds to RCS messages, the complexity of which challenges the filtering technology because each of them contains hundreds of fields with various types of data, such as texts, images and videos. Among the data, the hundreds of fields of text data contain the main content, which is adequate and more efficient for combating spam. This paper first discusses the text fields, which possibly contain spam information, then use the hidden Markov model (HMM) to weight the fields and finally use convolutional neural network (CNN) to classify the RCS messages. In the HMM step, the text fields are treated differently. The short texts of these fields are represented as feature weight sequences extracted by a feature extraction algorithm based on a probability density function. Then, the proposed HMM learns the weight sequence and produces a proper weight for each short text. Other text fields with fewer words are also weighted by the feature extraction algorithm. In the CNN step, all these feature weights first construct the RCS message matrix. The matrices of the training RCS messages are used as the CNN model inputs for learning and the matrices of testing messages are used as the trained CNN model inputs for RCS message property prediction. Four optimization technologies are introduced into the CNN classification process. Promising experiment results are achieved on the real industrial data.https://www.mdpi.com/2076-3417/11/14/6350rich communication suite (RCS)5Ganti-spamhidden Markov model (HMM)convolutional neural network (CNN) |
| spellingShingle | Bibu Gao Wenqiang Zhang A Method of Combining Hidden Markov Model and Convolutional Neural Network for the 5G RCS Message Filtering Applied Sciences rich communication suite (RCS) 5G anti-spam hidden Markov model (HMM) convolutional neural network (CNN) |
| title | A Method of Combining Hidden Markov Model and Convolutional Neural Network for the 5G RCS Message Filtering |
| title_full | A Method of Combining Hidden Markov Model and Convolutional Neural Network for the 5G RCS Message Filtering |
| title_fullStr | A Method of Combining Hidden Markov Model and Convolutional Neural Network for the 5G RCS Message Filtering |
| title_full_unstemmed | A Method of Combining Hidden Markov Model and Convolutional Neural Network for the 5G RCS Message Filtering |
| title_short | A Method of Combining Hidden Markov Model and Convolutional Neural Network for the 5G RCS Message Filtering |
| title_sort | method of combining hidden markov model and convolutional neural network for the 5g rcs message filtering |
| topic | rich communication suite (RCS) 5G anti-spam hidden Markov model (HMM) convolutional neural network (CNN) |
| url | https://www.mdpi.com/2076-3417/11/14/6350 |
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