How AI Can be Used for Governance of Messaging Services: A Study on Spam Classification Leveraging Multi-Channel Convolutional Neural Network
Over the past decade, there has been a meteoric evolution in Internet Messaging Services and although these services have become ingrained in our everyday life, SMS service remains an essential form of communication service. The omnipresence of SMS has also given rise to unsolicited and junk message...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | International Journal of Information Management Data Insights |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667096822000908 |
| _version_ | 1797847678074224640 |
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| author | Gopalkrishna Waja Gaurang Patil Charmee Mehta Sonali Patil |
| author_facet | Gopalkrishna Waja Gaurang Patil Charmee Mehta Sonali Patil |
| author_sort | Gopalkrishna Waja |
| collection | DOAJ |
| description | Over the past decade, there has been a meteoric evolution in Internet Messaging Services and although these services have become ingrained in our everyday life, SMS service remains an essential form of communication service. The omnipresence of SMS has also given rise to unsolicited and junk messages which has motivated researchers to use machine learning and deep learning to detect such spam messages. Studies using deep learning have shown promising results for spam classification, and in this paper, extending these studies, we have proposed a Multi-Channel CNN architecture with static and dynamic embeddings for SMS spam classification. UCI’s SMS spam collection dataset along with several personally collected text messages are used to create a rich dataset for training the models. The proposed model has an accuracy of 96.12% and overcomes certain disadvantages associated with some of the state-of-the-art deep learning models. |
| first_indexed | 2024-04-09T18:15:19Z |
| format | Article |
| id | doaj.art-17d77d9a3e0c4cb1a12b6186724861e0 |
| institution | Directory Open Access Journal |
| issn | 2667-0968 |
| language | English |
| last_indexed | 2024-04-09T18:15:19Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Information Management Data Insights |
| spelling | doaj.art-17d77d9a3e0c4cb1a12b6186724861e02023-04-13T04:27:20ZengElsevierInternational Journal of Information Management Data Insights2667-09682023-04-0131100147How AI Can be Used for Governance of Messaging Services: A Study on Spam Classification Leveraging Multi-Channel Convolutional Neural NetworkGopalkrishna Waja0Gaurang Patil1Charmee Mehta2Sonali Patil3Department of Information Technology, K.J. Somaiya College of Engineering, Somaiya Vidyavihar University, Vidyanagri, Vidyavihar East, Mumbai 400077, IndiaCorresponding author.; Department of Information Technology, K.J. Somaiya College of Engineering, Somaiya Vidyavihar University, Vidyanagri, Vidyavihar East, Mumbai 400077, IndiaDepartment of Information Technology, K.J. Somaiya College of Engineering, Somaiya Vidyavihar University, Vidyanagri, Vidyavihar East, Mumbai 400077, IndiaDepartment of Information Technology, K.J. Somaiya College of Engineering, Somaiya Vidyavihar University, Vidyanagri, Vidyavihar East, Mumbai 400077, IndiaOver the past decade, there has been a meteoric evolution in Internet Messaging Services and although these services have become ingrained in our everyday life, SMS service remains an essential form of communication service. The omnipresence of SMS has also given rise to unsolicited and junk messages which has motivated researchers to use machine learning and deep learning to detect such spam messages. Studies using deep learning have shown promising results for spam classification, and in this paper, extending these studies, we have proposed a Multi-Channel CNN architecture with static and dynamic embeddings for SMS spam classification. UCI’s SMS spam collection dataset along with several personally collected text messages are used to create a rich dataset for training the models. The proposed model has an accuracy of 96.12% and overcomes certain disadvantages associated with some of the state-of-the-art deep learning models.http://www.sciencedirect.com/science/article/pii/S2667096822000908Multi-channelConvolutional neural networkSpam classificationWord embeddingsNatural language processingSMS text messages |
| spellingShingle | Gopalkrishna Waja Gaurang Patil Charmee Mehta Sonali Patil How AI Can be Used for Governance of Messaging Services: A Study on Spam Classification Leveraging Multi-Channel Convolutional Neural Network International Journal of Information Management Data Insights Multi-channel Convolutional neural network Spam classification Word embeddings Natural language processing SMS text messages |
| title | How AI Can be Used for Governance of Messaging Services: A Study on Spam Classification Leveraging Multi-Channel Convolutional Neural Network |
| title_full | How AI Can be Used for Governance of Messaging Services: A Study on Spam Classification Leveraging Multi-Channel Convolutional Neural Network |
| title_fullStr | How AI Can be Used for Governance of Messaging Services: A Study on Spam Classification Leveraging Multi-Channel Convolutional Neural Network |
| title_full_unstemmed | How AI Can be Used for Governance of Messaging Services: A Study on Spam Classification Leveraging Multi-Channel Convolutional Neural Network |
| title_short | How AI Can be Used for Governance of Messaging Services: A Study on Spam Classification Leveraging Multi-Channel Convolutional Neural Network |
| title_sort | how ai can be used for governance of messaging services a study on spam classification leveraging multi channel convolutional neural network |
| topic | Multi-channel Convolutional neural network Spam classification Word embeddings Natural language processing SMS text messages |
| url | http://www.sciencedirect.com/science/article/pii/S2667096822000908 |
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