Page 167 - 2024-Vol20-Issue2
P. 167
163 | Murad & Alasadi
[40] B. Noh, H. Park, S. Lee, and S. H. Nam, “Vision-based [52] M. H. Ismail, S. A. Dawwd, and F. H. Ali, “Dynamic
pedestrian’s crossing risky behavior extraction and anal- hand gesture recognition of arabic sign language using
ysis for intelligent mobility safety system,” Sensors, deep convolutional neural networks,” Indones. J. Electr.
vol. 22, no. 9, 2022. Eng. Comput. Sci., vol. 25, pp. 952–962, 2022.
[41] M. K. Hu, “Visual pattern recognition by moment invari- [53] N. Rajawat, N. Gupta, and S. Lalwani, “A comprehen-
ants,” IRE Transactions on Information Theory, vol. 8, sive review of hidden markov model applications in pre-
no. 2, pp. 179–187, 1962. dicting human mobility patterns,” International Journal
of Swarm Intelligence, vol. 6, no. 1, pp. 24–47, 2021.
[42] S. Katoch, V. Singh, and U. S. Tiwary, “Indian sign
language recognition system using surf with svm and [54] D. Sarma and M. K. Bhuyan, “Methods, databases and
cnn,” Array, vol. 14, 2022. recent advancement of vision-based hand gesture recog-
nition for hci systems: A review,” SN Computer Science,
[43] Z. Ren, F. Fang, N. Yan, and Y. Wu, “State of the art in vol. 2, no. 6, 2021.
defect detection based on machine vision,” International
Journal of Precision Engineering and Manufacturing- [55] S. Mandal, Z. Li, T. Chatterjee, K. Khanna, K. Montoya,
Green Technology, vol. 9, no. 2, pp. 661–691, 2022. L. Dai, C. Petersen, L. Li, M. Tewari, A. Johnson-Buck,
and N. G. Walter, “Direct kinetic fingerprinting for high-
[44] N. Mirehi, M. Tahmasbi, and A. T. Targhi, “Hand ges- accuracy single-molecule counting of diverse disease
ture recognition using topological features,” Multimedia biomarkers,” Accounts of Chemical Research, vol. 54,
Tools and Applications, vol. 78, pp. 13361–13386, 2019. no. 2, pp. 388–402, 2020.
[45] M. Wagh and P. K. Nanda, “Decision-theoretic rough [56] J. Arora, K. Khatter, and M. Tushir, “Fuzzy c-means
sets based automated scheme for object and background clustering strategies: A review of distance measures,”
classification in unevenly illuminated images,” Applied in Software Engineering: Proceedings of CSI 2015,
Soft Computing, vol. 119, 2022. pp. 153–162, 2019.
[46] W. Chen, C. Yu, C. Tu, Z. Lyu, J. Tang, S. Ou, Y. Fu, [57] R. S. Gaikwad and L. S. Admuthe, “A review of vari-
and Z. Xue, “A survey on hand pose estimation with ous sign language recognition techniques,” in Modeling,
wearable sensors and computer-vision-based methods,” Simulation, and Optimization: Proceedings of CoMSO
Sensors, vol. 20, no. 4, p. 1074, 2020. 2021, pp. 111–126, jun 2022.
[47] J. Qi, K. Xu, and X. Ding, “Approach to hand posture [58] K. Taunk, S. De, S. Verma, and A. Swetapadma, “A brief
recognition based on hand shape features for a human- review of the nearest neighbor algorithm for learning
robot interaction,” Complex & Intelligent Systems, 2021. and classification,” in 2019 International Conference
on Intelligent Computing and Control Systems (ICCS),
[48] M. Al-Hammadi, G. Muhammad, W. Abdul, M. Alsu- pp. 1255–1260, IEEE, may 2019.
laiman, M. A. Bencherif, and M. A. Mekhtiche, “Hand
gesture recognition for sign language using 3dcnn,” [59] S. Ghosh, A. Dasgupta, and A. Swetapadma, “A study
IEEE Access, vol. 8, pp. 79491–79509, 2020. on support vector machine-based linear and non-linear
pattern classification,” in 2019 International Conference
[49] A. Thakur and A. Konde, “Fundamentals of neural net- on Intelligent Sustainable Systems (ICISS), pp. 24–28,
works,” International Journal for Research in Applied IEEE, feb 2019.
Science and Engineering Technology, vol. 9, pp. 407–
426, 2021. [60] M. Yu, J. Jia, C. Xue, G. Yan, Y. Guo, and Y. Liu, “A
review of sign language recognition research,” Journal
[50] T. H. Maung, “Real-time hand tracking and gesture of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 3879–
recognition system using neural networks,” Interna- 3898, 2022.
tional Journal of Computer and Information Engineer-
ing, vol. 3, no. 2, pp. 315–319, 2009. [61] U. Moser and D. Schramm, “Multivariate dynamic time
warping in automotive applications: A review,” Intelli-
[51] E. Stergiopoulou and N. Papamarkos, “Hand gesture gent Data Analysis, vol. 23, no. 3, pp. 535–553, 2019.
recognition using a neural network shape fitting tech-
nique,” Engineering Applications of Artificial Intelli- [62] D. Bhatt, C. Patel, H. Talsania, J. Patel, R. Vaghela,
gence, vol. 22, no. 8, pp. 1141–1158, 2009. S. Pandya, K. Modi, and H. Ghayvat, “Cnn variants for