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Hesam Akbari

Title: Teaching Fellow

Department: Information Science

College: College of Information

Curriculum Vitae

Curriculum Vitae Link

Current Scheduled Teaching

INFO 4670.401Data Analysis and Knowledge DiscoverySummer 10W 2025 Syllabus
INFO 4670.402Data Analysis and Knowledge DiscoverySummer 10W 2025 Syllabus
Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Previous Scheduled Teaching

DTSC 3020.020Introduction to Computation with PythonSpring 2025 Syllabus SPOT
Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

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
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Overall
Summative Rating		 Challenge and
Engagement Index		 Response Rate

out of 5
out of 7		 %
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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