Open Access
Issue
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
Article Number 00088
Number of page(s) 13
DOI https://doi.org/10.1051/bioconf/20249700088
Published online 05 April 2024
  • Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., & Wang, Y. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102168. [CrossRef] [Google Scholar]
  • Forest, F., Lebbah, M., Azzag, H., & Lacaille, J. (2021). Deep embedded self-organizing maps for joint representation learning and topology-preserving clustering. Neural Computing and Applications, 33(24), 17439–17469. [CrossRef] [Google Scholar]
  • Abkenar, S. B., Kashani, M. H., Mahdipour, E., & Jameii, S. M. (2021). Big data analytics meets social media: A systematic review of techniques, open issues, and future directions. Telematics and informatics, 57, 101517. [CrossRef] [PubMed] [Google Scholar]
  • Terán, L., & Terán, L. (2020). A literature review for recommender systems techniques used in microblogs. Dynamic Profiles for Voting Advice Applications: An Implementation for the 2017 Ecuador National Elections, 27–47. [Google Scholar]
  • Castillejo, E., Almeida, A., & López-de-Ipina, D. (2012). Social network analysis applied to recommendation systems: Alleviating the cold-user problem. In Ubiquitous Computing and Ambient Intelligence: 6th International Conference, UCAmI 2012, Vitoria-Gasteiz, Spain, December 3-5, 2012. Proceedings 6 (pp. 306–313). Springer Berlin Heidelberg. [Google Scholar]
  • Al, A., & Amin, M. Z. (2019). An Intuitive Guide of Self Organizing Maps with PracticalImplementation in Minisom. [Google Scholar]
  • Yang, H. C., Lee, C. H., & Wu, C. Y. (2018). Sentiment discovery of social messages using self- organizing maps. Cognitive Computation, 10, 1152–1166. [CrossRef] [Google Scholar]
  • Han, J., Kamber, M., & Pei, J. (2011). Data mining: concepts and techniques (3rd ed.). Morgan Kaufmann. [Google Scholar]
  • Relia, K., Akbari, M., Duncan, D., & Chunara, R. (2018). Socio-spatial self-organizing maps: usingsocial media to assess relevant geographies for exposure to social processes. Proceedings of the ACM on human-computer interaction, 2(CSCW), 1–23. [CrossRef] [Google Scholar]
  • Hawas, A. Y., Naser, A. H., & Jalali, M. (2023, September). Location-Based in recommendation system using naive Bayesian algorithm. In AIP Conference Proceedings (Vol. 2845, No. 1). AIP Publishing. [Google Scholar]
  • Cottrell, M., Olteanu, M., Rossi, F., & Villa-Vialaneix, N. N. (2018). Self-organizing maps, theoryand applications. Revista de Investigacion Operacional, 39(1), 1–22. [Google Scholar]
  • Rani, S., & Kumar, M. (2020). Social media video summarization using multi-Visual features and Kohnen's Self Organizing Map. Information Processing & Management, 57 (3), 102190. [CrossRef] [MathSciNet] [Google Scholar]
  • Couronne, T., Beuscart, J. S., & Chamayou, C. (2013). Self-Organizing Map and social networks: Unfolding online social popularity. arXiv preprint arXiv:1301.6574. [Google Scholar]
  • Relia, K., Akbari, M., Duncan, D., and Chunara, R. (2018). Socio-spatial self-organizing maps: Using social media to evaluate relevant geographies' exposure to social processes. Proceedings of the ACM on HumanComputer Interaction, 2 (CSCW), 1–23. [CrossRef] [Google Scholar]
  • Nazari, Z., Kang, D., Asharif, M. R., Sung, Y., & Ogawa, S. (2015, November). A new hierarchical clustering algorithm. In 2015 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) (pp. 148–152). IEEE. [Google Scholar]
  • Shujaaddeen, A., Ba-Alwi, F. M., & Al-Gaphari, G. (2024). A New Machine Learning Model for Detecting levels of Tax Evasion Based on Hybrid Neural Network. International Journal of Intelligent Systems and Applications in Engineering, 12(11s), 450–468. [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.