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Mr. Hesam Akbari

Teaching Fellow

University of North Texas

Department of Information Science

Email: Hesam.Akbari@unt.edu

Education

  • Diploma (International), Islamic Azad University - Iran, 2018
    Major: Biomedical Engineering (Bioelectrics)

Teaching

Teaching Experience

    University of North Texas

  • DTSC 3020 - Introduction to Computation with Python, 1 course.
  • INFO 4670 - Data Analysis and Knowledge Discovery, 2 courses.

Research

Published Intellectual Contributions

    Book Chapter

  • Akbari, H., Korani, W. (2024). Early Detection of Depression and Alcoholism Disorders by EEG Signal. Communications in Computer and Information Science. 439-452. Springer Nature Singapore. https://doi.org/10.1007/978-981-99-8141-0_33
  • Sadiq, M.T., Akbari, H., Siuly, S., Li, Y., Wen, P. (2022). Fractional Fourier Transform Aided Computerized Framework for Alcoholism Identification in EEG. Lecture Notes in Computer Science. 100-112. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-20627-6_10
  • Too, J., Sadiq, A.S., Akbari, H., Mong, G.R., Mirjalili, S. (2022). Hybrid Generalized Normal Distribution Optimization with Sine Cosine Algorithm for Global Optimization. Lecture Notes on Data Engineering and Communications Technologies. 35-42. Springer Nature Singapore. https://doi.org/10.1007/978-981-19-2948-9_4
  • Akbari, H., Sadiq, M.T., Siuly, S., Li, Y., Wen, P. (2021). An Automatic Scheme with Diagnostic Index for Identification of Normal and Depression EEG Signals. Lecture Notes in Computer Science. 59-70. Springer International Publishing. https://doi.org/10.1007/978-3-030-90885-0_6

    • Conference Proceeding

  • Akbari, H., Korani, W., Ding, J., Rostami, R., Kazemi, R. (2024). TOP-EEG: a robust software to predict the outcomes of therapies for depression using EEG signals in DGMD domain. International Conference on Neural Information Processing (ICONIP2024).
  • Akbari, H., Korani, W., Ding, J., Rostami, R., Kazemi, R. (2024). UNT-AT: A Robust Software to Predict the Outcome of Depression Therapies Using EEG Signals. International Conference on Neural Information Processing (ICONIP2024).
  • Ghofrani, S., Akbari, H., Mao, K., Jiang, X. (2019). Comparing nonlinear features extracted in EEMD for discriminating focal and non-focal EEG signals. Tenth International Conference on Signal Processing Systems. 43. SPIE. https://doi.org/10.1117/12.2523445

    • Journal Article

  • Akbari, H., Sadiq, M.T., Siuly, S., Li, Y., Wen, P. (2022). Identification of normal and depression EEG signals in variational mode decomposition domain. Other. 10 (1) Springer Science and Business Media LLC. https://doi.org/10.1007/s13755-022-00187-7
  • Akbari, H., Sadiq, M.T., Rehman, A.U. (2021). Classification of normal and depressed EEG signals based on centered correntropy of rhythms in empirical wavelet transform domain. Other. 9 (1) Springer Science and Business Media LLC. https://doi.org/10.1007/s13755-021-00139-7
  • Sadiq, M.T., Akbari, H., Siuly, S., Yousaf, A., Rehman, A.U. (2021). A novel computer-aided diagnosis framework for EEG-based identification of neural diseases. Computers in Biology and Medicine. 138 104922. Elsevier BV. https://doi.org/10.1016/j.compbiomed.2021.104922
  • Akbari, H., Sadiq, M.T., Ur Rehman, A., Ghazvini, M., Naqvi, R.A., Payan, M., Bagheri, H., Bagheri, H. (2021). Depression recognition based on the reconstruction of phase space of EEG signals and geometrical features. Other. 179 108078. Elsevier BV. https://doi.org/10.1016/j.apacoust.2021.108078
  • Akbari, H., Sadiq, M.T. (2021). Detection of focal and non-focal EEG signals using non-linear features derived from empirical wavelet transform rhythms. Other. 44 (1) 157-171. Springer Science and Business Media LLC. https://doi.org/10.1007/s13246-020-00963-3
  • Akbari, H., Sadiq, M.T., Payan, M., Esmaili, S.S., Baghri, H., Bagheri, H. (2021). Depression Detection Based on Geometrical Features Extracted from SODP Shape of EEG Signals and Binary PSO. Other. 38 (1) 13-26. International Information and Engineering Technology Association. https://doi.org/10.18280/ts.380102
  • Akbari, H., Ghofrani, S. (2019). Fast and Accurate Classification F and NF EEG by Using SODP and EWT. Other. 11 (11) 29-35. MECS Publisher. https://doi.org/10.5815/ijigsp.2019.11.04