Predatory Journal Detection using Machine learning and Django

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Description:

Make audience aware about rising predatory journals and how machine learning can be used to detect predatory journals.

Predatory Journals take advantage of authors by asking them to publish by paying their fees, without providing peer-review or editing services. Because predatory publishers do not follow the proper academic standards for publishing, they usually offer a quick turnaround on publishing a manuscript. They are regarded as predatory because scholars are tricked into publishing with them, although some authors may be aware that the journal is poor quality or even fraudulent. In contrast, high-quality academic journals take longer to publish articles because they go through a proper peer review and copy-editing process. To prevent these frauds we've created an application by which we can determine whether a journal is predatory or not. The application is created with the help of python framework Django which is used for maintaining individual journal records with its parameters. The determination of a predatory journal is done by using a machine learning algorithm, i.e, Decision tree algorithm. Here the model is trained with journal name and its parameters and saved as a file so that later on whenever needed, we can load the trained model directly without re-training the model. After combining the database and ML algorithm, we get the journal details with the result after searching the journal name in the user interface.

Topics which will be covered in the presentation are

  1. Introduction to publishing and ethics [3 min]
  2. Predatory journals and their characteristics. [4 min]
  3. Building the data-set and parameters to consider. [5 min]
  4. Developing Machine learning model for the classification based on the above parameters. [8 min]
  5. Integrating the machine learning model into Django Framework and building web application. [5 min]
  6. Conclusion and QA. [5 min]

Prerequisites:

Basic understanding of python and machine learning

Video URL:

https://youtu.be/XklyabrlpSA

Content URLs:

Presentation

Speaker Info:

Rohit Dhulubulu

Final year Computer Science student from KLS Gogte Institute of Technology, Belgaum, Karnataka.

He has interest in web development, game development.

Gaurav Bhasme

Final year Computer Science student from KLS Gogte Institute of Technology, Belgaum, Karnataka.

He has interest in web development.

Speaker Links:

Rohit Dhulubulu

LinkedIn

GitHub

Gaurav Bhasme

LinkedIn

GitHub

Section: Web development
Type: Talks
Target Audience: Intermediate
Last Updated: