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EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 11, Issue 1 (January 2024), Pages: 150-160


 Original Research Paper

Optimizing semantic error detection through weighted federated machine learning: A comprehensive approach


 Naila Samar Naz 1, Sagheer Abbas 1, Muhammad Adnan Khan 2, 3, 4, *, Zahid Hassan 1, Mazhar Bukhari 5, Taher M. Ghazal 6, 7


 1School of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan
 2School of Computing, Skyline University College, Sharjah, UAE
 3Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam, South Korea
 4Riphah School of Computing and Innovation, Faculty of Computing, Riphah International University, Lahore, Pakistan
 5Department of Computer Sciences, The Institute of Management Sciences, Lahore, Pakistan
 6Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
 7Applied Science Research Center, Applied Science Private University, Amman, Jordan

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Recently, the improvement of network technology and the spread of digital documents have made the technology for automatically correcting English texts very important. In English language processing, finding and fixing mistakes in the meaning of words is a very interesting and important job. It is also important to fix wrong data in cleaning data. Usually, systems that find errors need the user to set up rules or statistical information. To build a good system for finding mistakes in meaning, it must be able to spot errors and odd details. Many things can make the meaning of a sentence unclear. Therefore, this study suggests using a system that finds semantic errors with the help of weighted federated machine learning (SED-WFML). This system also connects to the web ontology's classes and features that are important for the area of knowledge in natural language processing (NLP) text documents. This helps identify correct and incorrect sentences in the document, which can be used for many purposes like checking documents automatically, translating, and more. During its training and checking stages, the new model identified correct and incorrect sentences with an accuracy of 95.6% and 94.8%, respectively, which is better than earlier methods.

 © 2024 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (


 Artificial neural network, Semantic error detection, Federated learning, Natural language processing, SED-WFML

 Article history

 Received 30 August 2023, Received in revised form 18 December 2023, Accepted 9 January 2024


No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.


 Naz NS, Abbas S, Khan MA, Hassan Z, Bukhari M, and Ghazal TM (2024). Optimizing semantic error detection through weighted federated machine learning: A comprehensive approach. International Journal of Advanced and Applied Sciences, 11(1): 150-160

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 Fig. 1 Fig. 2 Fig. 3 Fig. 4 


 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 Table 9


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