Using Artificial Intelligence Approaches to Categorise Lecture Notes

Lecture materials cover a broad variety of documents ranging from e-books, lecture notes, handouts, research papers and lab reports amongst others. Downloaded from the Internet, these documents generally go in the Downloads folder or other folders specified by the students. Over a certain period of time, the folders become so messy that it becomes quite difficult to find our way through them. Sometimes files downloaded from the Internet are saved without the certainty that they will be used or revert to in the future. Documents are scattered all over the computer system, making it very troublesome and time consuming for the user to search for a particular file. Another issue that adds up to the difficulty is the improper naming conventions. Certain files bear names that are totally irrelevant to their contents. Therefore, the user has to open these documents one by one and go through them to know what the files are about. One solution to this problem is a file classifier. In this paper, a file classifier will be used to organise the lecture materials into eight different categories, thus easing the tasks of the students and helping them to organise the files and folders on their workstations. Modules each containing about 25 files were used in this study. Two machine learning techniques were used, namely, decision trees and support vector machines. For most categories, it was found that decision trees outperformed SVM.

@article{Baijoo2018,
  title     = {Using Artificial Intelligence Approaches to Categorise Lecture Notes},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {8},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Naushine Bibi Baijoo and Khusboo Bharossa and Somveer Kishnah and Sameerchand Pudaruth},
  doi       = {10.14569/IJACSA.2018.090842},
  url       = {https://doi.org/10.14569/IJACSA.2018.090842}
}