Page 129 - 2024-Vol20-Issue2
P. 129

125 |                                                             Assim & Mahmood

[23] A. Massot-Tarru´s and S. M. Mirsattari, “Roles of fmri       [34] W. H. Park, N. M. F. Qureshi, and D. R. Shin, “An ef-
      and wada tests in the presurgical evaluation of language          fective 3d text recurrent voting generator for metaverse,”
      functions in temporal lobe epilepsy,” Frontiers in Neu-           IEEE Transactions on Affective Computing, 2022.
      rology, vol. 13, p. 884730, 2022.
                                                                  [35] S. K. Jawad and M. Alaziz, “Human activity and ges-
[24] O. S. Atiyah and S. H. Thalij, “A comparison of coivd-             ture recognition based on wifi using deep convolutional
      19 cases classification based on machine learning ap-             neural networks,” Iraqi Journal for Electrical And Elec-
      proaches,” Iraqi Journal for Electrical and Electronic            tronic Engineering, vol. 18, no. 2, 2022.
      Engineering, vol. 18, no. 1, pp. 139–143, 2022.
                                                                  [36] R. Chai, S. H. Ling, P. P. San, G. R. Naik, T. N. Nguyen,
[25] E. Tuncer and E. D. Bolat, “Classification of epileptic            Y. Tran, A. Craig, and H. T. Nguyen, “Improving eeg-
      seizures from electroencephalogram (eeg) data using               based driver fatigue classification using sparse-deep be-
      bidirectional short-term memory (bi-lstm) network ar-             lief networks,” Frontiers in neuroscience, vol. 11, p. 103,
      chitecture,” Biomedical Signal Processing and Control,            2017.
      vol. 73, p. 103462, 2022.
                                                                  [37] L. Va?reka and P. Mautner, “Stacked autoencoders for the
[26] G. Y. Abbass and A. F. Marhoon, “Iraqi license plate               p300 component detection,” Frontiers in neuroscience,
      detection and segmentation based on deep learning,”               vol. 11, p. 302, 2017.
      Iraqi Journal for Electrical and Electronic Engineer-
      ing, vol. 17, no. 2, pp. 102–107, 2021.                     [38] A. Ditthapron, N. Banluesombatkul, S. Ketrat,
                                                                        E. Chuangsuwanich, and T. Wilaiprasitporn, “Universal
[27] E. B. Assi, D. K. Nguyen, S. Rihana, and M. Sawan,                 joint feature extraction for p300 eeg classification using
      “Towards accurate prediction of epileptic seizures: A             multi-task autoencoder,” IEEE access, vol. 7, pp. 68415–
      review,” Biomedical Signal Processing and Control,                68428, 2019.
      vol. 34, pp. 144–157, 2017.
                                                                  [39] N. A. Noori and A. A. Yassin, “Towards for designing in-
[28] J. B. Romaine, M. Pereira Mart´in, J. R. Salvador Or-              telligent health care system based on machine learning.,”
      tiz, and J. M. Manzano Crespo, “Eeg—single-channel                Iraqi Journal for Electrical & Electronic Engineering,
      envelope synchronisation and classification for seizure           vol. 17, no. 2, 2021.
      detection and prediction,” Brain Sciences, vol. 11, no. 4,
      p. 516, 2021.                                               [40] V. Gabeff, T. Teijeiro, M. Zapater, L. Cammoun,
                                                                        S. Rheims, P. Ryvlin, and D. Atienza, “Interpreting deep
[29] M. Abdulla and A. Marhoon, “Deep learning and iot for              learning models for epileptic seizure detection on eeg
      monitoring tomato plant,” Iraqi Journal for Electrical            signals,” Artificial intelligence in medicine, vol. 117,
      and Electronic Engineering, vol. 19, 2023.                        p. 102084, 2021.

[30] S. A. Thamer and M. A. Alshmmri, “Detection of covid-        [41] Y. Pan, X. Zhou, F. Dong, J. Wu, Y. Xu, S. Zheng, et al.,
      19 using cad system depending on chest x-ray and ma-              “Epileptic seizure detection with hybrid time-frequency
      chine learning techniques,” Iraqi Journal for Electrical          eeg input: a deep learning approach,” Computational
      And Electronic Engineering, vol. 18, no. 2, 2022.                 and Mathematical Methods in Medicine, vol. 2022,
                                                                        2022.
[31] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learn-
      ing for electroencephalogram (eeg) classification tasks:    [42] W. Zhao, W. Zhao, W. Wang, X. Jiang, X. Zhang,
      a review,” Journal of neural engineering, vol. 16, no. 3,         Y. Peng, B. Zhang, G. Zhang, et al., “A novel deep
      p. 031001, 2019.                                                  neural network for robust detection of seizures using eeg
                                                                        signals,” Computational and mathematical methods in
[32] M. Natu, M. Bachute, S. Gite, K. Kotecha, A. Vidyarthi,            medicine, vol. 2020, 2020.
      et al., “Review on epileptic seizure prediction: machine
      learning and deep learning approaches,” Computational       [43] A. Abdelhameed and M. Bayoumi, “A deep learning ap-
      and Mathematical Methods in Medicine, vol. 2022,                  proach for automatic seizure detection in children with
      2022.                                                             epilepsy,” Frontiers in Computational Neuroscience,
                                                                        vol. 15, p. 650050, 2021.
[33] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet
      classification with deep convolutional neural networks,”    [44] Y. Ru, J. Li, H. Chen, J. Li, et al., “Epilepsy detection
      Communications of the ACM, vol. 60, no. 6, pp. 84–90,             based on variational mode decomposition and improved
      2017.
   124   125   126   127   128   129   130   131   132   133   134