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                  XI. CONCLUSION                                     [6] R. F. Pinto Jr, C. D. Borges, A. M. Almeida, and I. C.
                                                                          Paula Jr, “Static hand gesture recognition based on con-
The review emphasizes the importance and challenges of hand               volutional neural networks,” Journal of Electrical and
gesture recognition in various fields, such as human-computer             Computer Engineering, vol. 2019, no. 1, p. 4167890,
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hand modeling and feature extraction are crucial for capturing            trical engineering and computer science, vol. 18, no. 1,
and analyzing hand gestures. Machine learning algorithms                  pp. 49–55, 2020.
are essential for classifying and recognizing gestures based
on extracted features. Challenges in hand gesture recognition        [8] P. Das, T. Ahmed, and M. F. Ali, “Static hand gesture
include lighting variations, complex backgrounds, noise, and              recognition for american sign language using deep con-
real-time performance. The review acknowledges the need                   volutional neural network,” in 2020 IEEE Region 10
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The review provides valuable insights into the current state of      [9] I. Papastratis, C. Chatzikonstantinou, D. Konstantinidis,
hand gesture recognition, its applications, and the potential for         K. Dimitropoulos, and P. Daras, “Artificial intelligence
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between different communities.                                            p. 5843, 2021.

              CONFLICT OF INTEREST                                  [10] L. I. Khalaf, S. A. Aswad, S. R. Ahmed, B. Makki, and
                                                                          M. R. Ahmed, “Survey on recognition hand gesture by
The authors have no conflict of relevant interest to this article.        using data mining algorithms,” in 2022 International
                                                                          Congress on Human-Computer Interaction, Optimiza-
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