main| new issue| archive| editorial board| for the authors| publishing house|
ะ๓๑๑๊่้
Main page
New issue
Archive of articles
Editorial board
For the authors
Publishing house

 

 


ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 7. Vol. 31. 2025

DOI: 10.17587/it.31.370-378

K. Sh. Gurbanova, Chief Specialist, Training-Innovation Centre,
Institute of Information Technology of The Ministry of Science and Education of the Azerbaijan Republic, Baku, Azerbaijan

Gesture Recognition Algorithm Using Artificial Neural Network and Random Forest in Hybrid Working Environment

Received on 04.09.2024
Accepted on 25.09.2024

The dynamic development of information and communication technologies (ICT), artificial intelligence and digital technologies has contributed to the development of multi-level information systems. In addition to other fields, gesture-human-machine systems have improved and have a more user-friendly interface. The improvement of gesture-human-machine systems not only facilitates the communication and social adaptation of hearing-impaired people, but also expands user opportunities. Automatic recognition of gestures has enabled remote control of devices in the robotics field. The article clarifies the process of recording the parameters of the hand showing the gesture and the working principle of the recognition methods. The gesture recognition algorithm was analyzed using an artificial neural network and a random forest method in a hybrid working environment. Suggestions were made to eliminate the shortcomings that arose in the training process.
Keywords: gestures, artificial intelligence, recognition technologies, gesture-human-machine systems, machine learning, decision tree, neural network, classification

P. 370-378

Full text on eLIBRARY

 

References

  1. Gurbanova K. Sh. Overview of gesture control technologies, Informacionnye Tehnologii, 2024, vol. 30, no. 6, pp. 300—306.
  2. W. H. O. 2 February 2024, Deafness and hearing loss, available at: https://www.who.int/news-room/fact-sheets/detail/deafness-and-hearing-loss (date of access: 01/06/2024).
  3. Gurbanova K. Sh. Gesture language: hstory, development stages and current state, Problems of information society, 2018, no. 1, pp. 94—99.
  4. Gurbanova K. Sh. Research of stages, types of modeling and methods of gesture recognition, Informacionnye Tehnologii, 2024, vol. 30, no. 2, pp. 85—90.
  5. Gurbanova K. Sh., Abdullayeva F. J. A comparative analyญsis of gesture recording technologies and recognition methods, Problems of Information Society, 2023, vol. 14, no. 1, pp. 43—52.
  6. Chuikov A. V., Vulfin A. M. Gesture recognition system based on neural network technologies, Vestnik Ugatu, 2017, no. 3 (77), pp. 113—122.
  7. Chen G., Ge K. A fusion recognition method based on multi-feature hidden markov model for dynamic hand gesture, Computational Intelligence and Neuroscience, 2020, vol. 2020, no. 1, pp. 8871605.
  8. Bondareva V. E., Chernomorova T. S. Application of neural networks for sign language recognition, Intelligent Technologies in Science and Education Proceedings of the International Scientific and Practical Conference, November 24—25, 2023 Novocherkassk Conference organizers, 2023, pp. 59—65.
  9. Roshchina E. V., Kireev V. A. Overview of decision support algorithms and intelligent systems used in sign language recognition, Infocommunication technologies: current issues of the digital economy: Collection of scientific papers of the III International scientific and practical conference, January 25—26, 2023, Ekaterinญburg, 2023, pp. 139—144.
  10. Imamverdiev Yu. N., Aliguliyev R. M. Models and methods of managing information security of the electronic state, Baku, Publishing house "Information Technologies", 2021, 280 p.
  11. Cheng D. KNN algorithm with data-driven k value, Advanced Data Mining and Applications: 10th International Conference, Guilin, China. Proceedings 10, Springer International Publishing, December 19—21, 2014, pp. 499—512.
  12. Christopher A. K-Nearest Neighbor, Feb 2, 2021, available at: https://medium.com/swlh/k-nearest-neighbor-ca2593d7a3c4 (date of access: 09/06/2024).
  13. Escalante H. J., Morales E. F., Sucar L. E. A naive bayes baseline for early gesture recognition, Pattern Recognition Letters, 2016, vol. 73, pp. 91—99.
  14. Simatupang I., Pamungkas D., Risandriya S. Nave Bayes classifier for hand gestures recognition, Proceedings of the 3rd International Conference on Applied Engineering, 2020, pp. 110—114.
  15. Jia R., Yang L., Li Y., Xin Z. Gestures recognition of sEMG signal based on Random Forest, IEEE 16 th Conference on Industrial Electronics and Applicationes, 2021, pp. 1673—1678.
  16. Yusupova L. Kh., Popov D. V. Analysis of existing methods and technologies for solving the problem of gesture recognition, Innovative Ideas of Young Researchers, 2020, pp. 86—94.
  17. Kozyra P. S., Savelievb A. I. Analysis of the effectiveness of machine learning methods in the problem of gesture recognition based on the data of electromyographic signals, Computer Research and Modeling, 2021, vol. 13, no. 1, pp. 175—194.
  18. Tolmachev S. V., Grishina L. S. Research of the influence of the activation function of an artificial neural network in solving the problem of pattern recognition, Step into science, 2021, no. 4, pp. 77—82.
  19. Bogdanov M. R., Shakhmametova G. R., Oskin N. N., Kornilov A. V. Biometric identification based on gait, Systems of Control, Communication and Security, 2024, no. 1, pp. 191—212.
  20. Das S., Imtiaz M. S., Neom N. H., Siddique N., Wang H. A hybrid approach for Bangla sign language recognition using deep transfer learning odel with random forest classifier, Expert Systems with Applications, 2023, vol. 213, pp. 118914.
  21. Polin Y. A., Zudilova T. V., Ananchenko I. V., Voytiuk T. E. Decision trees in classification problems: application features and methods for improving the quality of classification, Modern high technologies, 2020, no. 9, pp. 59—63.
  22. Breiman L. Random Forests. Machine Learning, University of California Berkeley, 2001, vol. 45 (1), pp. 5—32.
  23. Akishina E. P., Ivanov V. V., Kryanev A. V., Prikazchikova A. S. The comparative analysis of decision trees and neural networks methods in the credit institutions classification problem, Vestnik natsional'nogo issledovatel'skogo yadernogo universiteta "MIFI", 2023, vol. 11, no. 6, pp. 442—449.
  24. Islam M. S., Mousumi S. S., Jessan N. A., Rabby A. S., Hossain S. A. Ishara-lipi: The first complete multipurposeopen access dataset of isolated characters for bangla sign language, Proceedings of the 2018 International Conference on Bangla Speech and Language Processing, Sylhet, Bangladesh, 21—22 September 2018, pp. 1—4.
  25. Abedin T., Prottoy K. S., Moshruba A., Hakim S. B. Bangla sign language recognition using a concatenated bdsl network, Computer Vision and Image Analysis for Industry 4.0, Chapman and Hall/CRC, 2023, pp. 76—86.
  26. Pavlov V. ภ ., Belov A. ภ ., Nguen V. T., Jovanovski N., Ovsyannikova A.S. Comparison of neural networks for suppression of multiplicative noise in images, Computer Optics, 2024, vol. 48, no. 3, pp. 425—431.
  27. Avilov M. I., Shichkina Yu. A., Kupriyanov M. S. Monitoring of an information and communication computer network using a neural network module, News of SPbGETU "LETI", 2020, no. 5, pp. 34—45.

To the contents