## 5 Conclusions and Future Work  
To filter spam RCS messages, this research proposed the method of combining the hidden Markov model and convolutional neural networks. This research first investigated the fields of RCS message data and chose 210 text fields that possibly contain spam information. Due to the complexity of RCS, the texts in these fields may be short texts or just one or two words. To weight all these fields properly, the HMM method was used for the short texts. They were first preprocessed to become word sequences, including the tokenization and the stop words removal steps. Then, the distinct words were weighted by the feature extraction algorithm based on the probability density function. The word sequences were transformed into the observation sequences by substituting their words with their weights. The hidden state sequences were generated according to the sign of each weight in the weight sequence. Such pairs of observation and the hidden states of the sequences fed the proposed HMM for training. While predicting, each testing short text was first preprocessed as well and was transformed into an observation sequence by substituting its words with the weights of the same words in the training set. Then, the trained HMM decoded the optimal hidden state sequence, which was used to weight the short text. The HMM method was performed on all short text fields. For other fields with fewer words, they were preprocessed and weighted by the feature extraction algorithm. When all the 210 fields are weighted, the RCS message matrix was formed. In the CNN step, the matrices of the training RCS messages fed the proposed CNN for deep learning. Then, the trained CNN produced the property of each testing RCS message based on its matrix. The RCS messages for the experiment were derived from the production environment of our cooperated company. The experiment showed promising results, such as 96.5% average accuracy, indicating prosperous future implementation of this study in the RCS industry.  
In the near future, the RCS service is expected to spread across the world to provide a secure, convenient and interactive user experience. Sufficient RCS messages will be used for filtering research and development. We will focus on improving accuracy and filtering speed by introducing HMM variants, hybrid models and other machine learning methods into this research field. We will also take advantage of whole RCS data, including images, videos, and so forth, for filtering.  
[PERSON] and [PERSON] conceived the idea and developed the algorithm; [PERSON] developed the experimental software; [PERSON] and [PERSON] performed the data analysis. [PERSON] prepared the manuscript; [PERSON] and [PERSON] wrote and made revisions to the paper. Both authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Restrictions apply to the availability of these data. Data was obtained from Dahan Tricom and are available at www.dahantc.com (accessed on 8 July 2021) with the permission of Dahan Tricom.  
**Conflicts of Interest:** The authors declare no conflict of interest.