Deypir, M., & Zoughi, T. (2025). Robust security risk estimation for android apps using nearest neighbor approach and hamming distance. Soft Computing, 29, 593–611. https://doi.org/10.1007/s00500-025-10489-z

Deypir, M., & Zoughi, T. (2024). Novel Security Metrics for Identifying Risky Unified Resource Locators (URLs). Iran J Sci Technol Trans Electr Eng, 48, 1209–1227. https://doi.org/10.1007/s40998-023-00690-x

Zoughi, T., & Deypir, M. (2024). Formal Verification of an Enhanced Deep Learning Model: Unveiling Computational Effectiveness in Speech Recognition. Scientia Iranica. https://doi.org/10.24200/sci.2024.63333.8341

Deypir, M., & Zoughi, T. (2024). Risk Score Computation for Android Mobile Applications Using the Twin k-NN Approach. Journal of Web Engineering, 23(4), 535–559. https://doi.org/10.13052/jwe1540-9589.2343

Zoughi, T., & Deypir, M. (2024). Mathematical analysis of AMRes: unlocking enhanced recognition across audio-visual domains. International Journal of Information Technology. https://doi.org/10.1007/s41870-024-01739-8

Afsharpour, P., Zoughi, T., Deypir, M., & Zoqi, M. J. (2024). Robust deep learning method for fruit decay detection and plant identification: enhancing food security and quality control. Frontiers in Plant Science, 15, 1366395. https://doi.org/10.3389/fpls.2024.1366395

Zoughi, T., & Homayounpour, M. M. (2020). Adaptive Windows Multiple Deep Residual Networks for Speech Recognition. Expert Systems with Applications, 139, 112840. https://doi.org/10.1016/j.eswa.2019.112840

Zoughi, T., & Homayounpour, M. M. (2018). DBMiP: A Pre-training Method for Information Propagation over Deep Networks. Computer Speech & Language, 55, 1–19. https://doi.org/10.1016/j.csl.2018.10.001

Zoughi, T., & Homayounpour, M. M. (2019). A Gender Aware Deep Neural Network Structure for Speech Recognition. Iranian Journal of Science and Technology, 43, 635–644. https://doi.org/10.1007/s40998-019-00177-8

Zoughi, T., & Boostani, R. (2012). A wavelet-based estimating depth of anesthesia. Engineering Applications of Artificial Intelligence, 25(8), 1710–1722.

Deypir, M., Alizadeh, S., Zoughi, T., & Boostani, R. (2011). Boosting a multi-linear classifier with application to visual lip reading. Expert Systems with Applications, 38(1), 941–948.

Sabeti, M., Boostani, R., & Zoughi, T. (2012). Using genetic programming to select the informative EEG-based features to distinguish schizophrenic patients. Neural Network World, 22(1), 3–11.

Deypir, M., Boostani, R., & Zoughi, T. (2012). Ensemble based multi-linear discriminant analysis with boosting and nearest neighbor. Scientia Iranica, 19(3), 654–661.

Deypir, M., Maarefi, A., & Zoughi, T. (2024). A Hybrid Multivariate Routing Approach for Improving Efficiency in VANETS. Karafan Journal, 21(1), 39–62. https://doi.org/10.48301/KSSA.2024.423334.2752

Zoughi, T., & Homayounpour, M. M. (2018). Adaptive Windows Convolutional Neural Network for Speech Recognition. Signal and Data Processing, 37(3), 13–29. http://dx.doi.org/10.29252/jsdp.15.3.13

Zoughi, T., & Boostani, R. (2011). Propose New Methods to Determine Depth of Anesthesia. Journal of Control, Khaje Nasir Toosi University of Technology.

Zoughi, M. J., & Zoughi, T. (2010). Prediction of COD and NH4+-N concentrations in Leachate from Lab-scale Landfill Bioreactors Using Artificial Neural Networks. Water and Wastewater, 21(2).