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iCACD: an intelligent deep learning model to categorise current affairs news article for efficient journalistic process

Author

Listed:
  • Sachin Kumar

    (University of Delhi)

  • Shivam Panwar

    (University of Delhi)

  • Jagvinder Singh

    (Delhi Technological University)

  • Anuj Kumar Sharma

    (University of Delhi)

  • Zairu Nisha

    (University of Delhi)

Abstract

In the present-day technology-driven world, information reaching at the individual’s doorstep sometimes becomes complex, haphazard and difficult to classify to get the insights. The endpoint consumer of the information requires processed information which is contextually suited to their needs, interests and is properly formatted and categorised. Interests and need-based categorization of news and stories would enable the user beforehand to further evaluate information deeply. For instances, the type current affairs related issues and news to read or not to read. This research work proposes an advanced current affairs classification model based on deep learning approaches called Intelligent Current Affairs Classification Using Deep Learning (iCACD). The proposed model is better than already proposed models based on machine learning approached which have been compared on accuracy and performance criteria. The proposed model is better in the following ways. Firstly, It is based on advanced deep neural network architecture. Secondly, the model advances the work to include both headline and body of the information/news articles rather than only processing headlines. Thirdly, A detailed comparative analysis and discussion on accuracy and performance with other machine leaning models have also been presented.

Suggested Citation

  • Sachin Kumar & Shivam Panwar & Jagvinder Singh & Anuj Kumar Sharma & Zairu Nisha, 2022. "iCACD: an intelligent deep learning model to categorise current affairs news article for efficient journalistic process," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2572-2582, October.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:5:d:10.1007_s13198-022-01666-6
    DOI: 10.1007/s13198-022-01666-6
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    References listed on IDEAS

    as
    1. Tarek Kanan & Edward A. Fox, 2016. "Automated arabic text classification with P-Stemmer, machine learning, and a tailored news article taxonomy," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(11), pages 2667-2683, November.
    2. Juan Wang & Jiangshe Zhang & Jie Zhao, 2016. "Texture Classification Using Scattering Statistical and Cooccurrence Features," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-6, February.
    3. Sachin Kumar & Jagvinder Singh & Ompal Singh, 2020. "Ensemble-based extreme learning machine model for occupancy detection with ambient attributes," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 173-183, July.
    4. Sachin Kumar & Saibal K. Pal & Ram Pal Singh, 2018. "A Conceptual Architectural Design for Intelligent Health Information System: Case Study on India," Springer Proceedings in Business and Economics, in: P.K. Kapur & Uday Kumar & Ajit Kumar Verma (ed.), Quality, IT and Business Operations, pages 1-15, Springer.
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    Cited by:

    1. Sachin Kumar & Zairu Nisha & Jagvinder Singh & Anuj Kumar Sharma, 2022. "Sensor network driven novel hybrid model based on feature selection and SVR to predict indoor temperature for energy consumption optimisation in smart buildings," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 3048-3061, December.

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