1. LNU supervisors

1.13. Ahmed Taiye Mohammed

  • Home page: https://lnu.se/en/staff/ahmedtaiye.mohammed/
  • Co-supervisor of 1-2 theses

    • Topics

      • Unstructured text anomaly detection: Anomalous text is implicit knowledge that is distinctively different from the general contextual ideas (Hodge & Austin, 2004; Kamaruddin et al. 2012; Mahaputra et al. 2012).
      • Text mining: brings upon the contributions of different text analytical components and knowledge input from disciplines like AI, statistics, computer science, and machine learning. These results in decisions affecting fields like information retrieval, natural language processing, web mining, text classification/clustering (Aggarwal & Zhai, 2012).
      • Computational thinking (CT) for non-engineering pupils:  CT is primarily a way of thinking and acting, which can be exhibited using skills, which then can become the basis for performance-based assessments (Shute et al. 2017). 

    • References

      Aggarwal, C. C., & Zhai, C. (2012). An introduction to text mining. Mining Text Data, 1-10. https://doi.org/10.1007/978-1-4614-3223-4_1 

      Hodge, V., & Austin, J. (2004). A survey of outlier detection methodologies. Artificial Intelligence Review, 22(2), 85-126. https://doi.org/10.1023/b:aire.0000045502.10941.a9

      Kamaruddin, S. S., Hamdan, A. R., Bakar, A. A., & Mat Nor, F. (2012). Deviation detection in text using conceptual graph interchange format and error tolerance dissimilarity function. Intelligent Data Analysis, 16(3), 487-511. https://doi.org/10.3233/ida-2012-0535

      Mahapatra, A., Srivastava, N., & Srivastava, J. (2012). Contextual anomaly detection in text data. Algorithms, 5(4), 469-489. https://doi.org/10.3390/a5040469

      Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142-158. https://doi.org/10.1016/j.edurev.2017.09.003