Feature Selection Algorithm-Based Data Filtration Model For "Data Journalism"

Authors

  • Syed Irfan Yaqoob Dr.Vishwanath Karad MIT World Peace University Pune INDIA.
  • Arun Prakash Dr.Vishwanath Karad MIT World Peace University Pune INDIA.
  • Anuradha Kanade Dr.Vishwanath Karad MIT World Peace University Pune INDIA.
  • Shantanu Kanade Symbiosis School For Online And Digital Learning (Symbiosis International University Pune INDIA
  • Neha Bharti Banaras Hindu University Varanasi INDIA

Keywords:

Data Journalism, Machine Learning, Feature Selection, Data filtration

Abstract

Journalism today has become a more dynamic and technology-oriented profession unlike conventional journalism. At the same time, it has also become a challenging task to handle and filter the huge amount of multimedia data received by media houses. It requires a larger workforce to manage and filter the data in order to make good news stories/packages. The media houses are now branching out to multiple platforms. The consumption of news is moving more towards the digital domain, people have also shifted their preference to consuming short, precise and relevant news in a personalized manner. Management of abundance Multimedia data in the media houses can be done in a concise amount of time (FSA) Feature Selection Algorithm is utilized to make the idea of data journalism more effective and efficient, we are offering a model that employs "FSA" to filter the requested (relevant) data from the enormous amount of in-house data.

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Published

24.03.2024

How to Cite

Yaqoob, S. I. ., Prakash, A. ., Kanade, A. ., Kanade, S. ., & Bharti, N. . (2024). Feature Selection Algorithm-Based Data Filtration Model For "Data Journalism". International Journal of Intelligent Systems and Applications in Engineering, 12(19s), 531–537. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5096

Issue

Vol. 12 No. 19s (2024)

Section

Research Article

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