Open Access
Issue |
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
|
|
---|---|---|
Article Number | 00136 | |
Number of page(s) | 13 | |
DOI | https://doi.org/10.1051/bioconf/20249700136 | |
Published online | 05 April 2024 |
- Al-Qudah, D., & Al-Ayyoub, M. (2018). SMS spam filtering using machine learning techniques: A systematic review. Journal of Network and Computer Applications, 103, 61–81. [Google Scholar]
- International Telecommunication Union. (2020). The Global Cybersecurity Index 2020. Retrieved from https://www.itu.int/en/ITUD/Cybersecurity/Pages/GCI.aspx [Google Scholar]
- Abdalla, H.I., Amer, A.A., & Ravana, S.D. (2023). BoW-based neural networks vs. cutting-edge models for single-label text classification. Neural Computing and Applications, 35(27), 20103–20116. [Google Scholar]
- Li, X., Xiang, Y., & Li, S. (2023). Combining convolutional and vision transformer structures for sheep face recognition. Computers and Electronics in Agriculture, 205, 107651. [CrossRef] [Google Scholar]
- Li, G., & Jung, J.J. (2023). Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges. Information Fusion, 91, 93–102. [CrossRef] [Google Scholar]
- Gawlikowski, J., Tassi, C.R.N., Ali, M., Lee, J., Humt, M., Feng, J., … & Zhu, X.X. (2023). A survey of uncertainty in deep neural networks. Artificial Intelligence Review, 56(Suppl 1), 1513-. [CrossRef] [Google Scholar]
- Almeida, T.A.; Hidalgo, J.M.G.; Yamakami, A. Contributions to the Study of SMS Spam Filtering: New Collection and Results. In Proceedings of the 11th ACM Symposium on Document Engineering, Mountain View, CA, USA, 19-22 September 2011; DocEng '11; Association for Computing Machinery: New York, NY, USA, 2011; pp. 259–262. [Google Scholar]
- Kumar, M., Sehgal, A.K., & Malhotra, R. (2018). SMS spam detection using machine learning techniques. In 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) (pp. 1–6). Bengaluru, India. Doi: 10.1109/CSITSS.2018.8768746 [Google Scholar]
- Navaney, P., Dubey, G., & Rana, A. (2018). SMS Spam Filtering Using Supervised Machine Learning Algorithms. In 2018 8th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 43–48). Noida, India. doi: 10.1109/CONFLUENCE.2018.8442564. [CrossRef] [Google Scholar]
- Zheng, B., Yoon, S.W., & Lam, S.S. (2014). Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Systems with Applications, 41(4), 1476–1482. [CrossRef] [Google Scholar]
- Sjarif, N.N.A., Azmi, N.F.M., Chuprat, S., Sarkan, H.M., Yahya, Y., & Sam, S.M. (2019). SMS spam message detection using term frequency-inverse document frequency and random forest algorithm. Procedia Computer Science, 161, 509–515. [CrossRef] [Google Scholar]
- Hosseinpour, S., & Shakibian, H. (2023, May). An Ensemble Learning Approach for SMS Spam Detection. In 2023 9th International Conference on Web Research (ICWR) (pp. 125–128). IEEE. [CrossRef] [Google Scholar]
- Ali, Z.H., Salman, H.M., & Harif, A.H. (2023). SMS Spam Detection Using Multiple Linear Regression and Extreme Learning Machines. Iraqi Journal of Science, 6342–6351. [CrossRef] [Google Scholar]
- Xia, T., & Chen, X. (2020). A Discrete Hidden Markov Model for SMS Spam Detection. Applied Sciences, 10(14), 5011. [CrossRef] [Google Scholar]
- Kumar, N., Singh, P.K., Kumar, A., & Tiwari, S. (2018). SMS Spam Detection Using Convolutional Neural Networks. In 2018 8th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 231–236). Noida. Doi: 10.1109/CONFLUENCE.2018.8442534 [Google Scholar]
- Raj, H., Weihong, Y., Banbhrani, S.K., & Dino, S.P. (2018, May). LSTM based short message service (SMS) modeling for spam classification. In Proceedings of the 2018 International Conference on Machine Learning Technologies (pp. 76–80). [CrossRef] [Google Scholar]
- Abayomi‐Alli, O., Misra, S., & Abayomi‐Alli, A. (2022). A deep learning method for automatic SMS spam classification: Performance of learning algorithms on indigenous dataset. Concurrency and Computation: Practice and Experience, 34(17), e6989. [CrossRef] [Google Scholar]
- Albayrak, Z., & Altunay, H.C. (2023). SMS Spam Detection System Based on Deep Learning Architectures in Turkish and English Messages. [Google Scholar]
- Ghourabi, A.; Mahmood, M.A.; Alzubi, Q.M. A Hybrid, C.N.N-LSTM Model for SMS Spam Detection in Arabic and English Messages. Future Internet 2020, 12, 156. https://doi.org/10.3390/fi12090156 [CrossRef] [Google Scholar]
- Srinivasarao, U., & Sharaff, A. (2023). SMS sentiment classification using an evolutionary optimization based fuzzy recurrent neural network. Multimedia Tools and Applications. Doi: 10.1007/s11042-023-15206-2 [Google Scholar]
- Seyedeh, T.S., Feizi-Derakhshi, M.R., & Razavi, S.N. (2016). Improvement of Persian Spam Filtering by Game Theory. International Journal of Advanced Computer Science and Applications, 7(6). [Google Scholar]
- Theodorus, A., Prasetyo, T.K., Hartono, R., & Suhartono, D. (2021, April). Short message service (sms) spam filtering using machine learning in Bahasa Indonesia. In 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT) (pp. 199–203). IEEE. [CrossRef] [Google Scholar]
- Lee, H., Jeong, S., Cho, S., & Choi, E. (2023). Visualization Technology and Deep-Learning for Multilingual Spam Message Detection. Electronics, 12(9). [Google Scholar]
- Zulqarnain, M., Sheikh, R., Hussain, S., Sajid, M., Abbas, S.N., Majid, M., & Ullah, U. (2024). Text Classification Using Deep Learning Models: A Comparative Review. Cloud Computing and Data Science, 80–96. [Google Scholar]
- Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., … & Ghayvat, H. (2021). CNN variants for computer vision: History, architecture, application, challenges and future scope. Electronics, 10(20), 2470. [CrossRef] [Google Scholar]
- Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A review of recurrent neural networks: LSTM cells and network architectures. Neural Computation, 31(7), 1235–1270. [CrossRef] [PubMed] [Google Scholar]
- Jabbooree, A.I., Khanli, L.M., Salehpour, P., & Pourbahrami, S. (2023). A novel facial expression recognition algorithm using geometry β-skeleton in fusion based on deep CNN. Image and Vision Computing, 134, 104677. [CrossRef] [Google Scholar]
- Khan, J., Ahmad, N., Khalid, S., Ali, F., & Lee, Y. (2023). Sentiment and Context-Aware Hybrid, D.N.N With Attention for Text Sentiment Classification. IEEE Access, 11, 28162–28179. [CrossRef] [Google Scholar]
- Krichen, M. (2023). Convolutional neural networks: A survey. Computers, 12(8), 151. [CrossRef] [Google Scholar]
- Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., … & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8, 1–74. [Google Scholar]
- Duan, K., Keerthi, S.S., Chu, W., Shevade, S.K., & Poo, A.N. (2003). Multicategory classification by soft-max combination of binary classifiers. In Multiple Classifier Systems (pp. 125–134). [CrossRef] [Google Scholar]
- Mewada, A., Dewang, R.K. (2023). A comprehensive survey of various methods in opinion spam detection. Multimedia Tools and Applications, 82, 13199–13239. Doi: 10.1007/s11042-022-13702-5 [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.