• A. M. Mir, M. Keshani, and S. Proksch. On the effect of transitivity and granularity on vulnerability propagation in the maven ecosystem. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), pages 201–211, March 2023
  • Mir, A. M., Latoškinas, E., Proksch, S., & Gousios, G., Type4Py: practical deep similarity learning-based type inference for Python. In Proceedings of the 44th International Conference on Software Engineering (ICSE’22), May 2022.
  • A. Mir, E. Latoskinas and G. Gousios, “ManyTypes4Py: A Benchmark Python Dataset for Machine Learning-based Type Inference,” in 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR), May 2021
  • A. Mir and Jalal A. Nasiri. Automatic opinion mining of movie reviews using robust twin support vector machine. In 4th Iranian Conference on Computational Linguistics. Institute for Humanities and Cultural Studies, February 2018 [in Persian]
  • A. Mir, Somayeh Fatahi, and Jalal A. Nasiri. Prediction of personality models in e-learning environments using twin support vector machine. In 2nd International Conference on Knowledge-Based Research in Computer Engineering and Information Technology. Allameh Tabataba’i University, September 2017 [in Persian]
  • A. Mir, Jalal A. Nasiri, and Somayeh Fatahi. Sentiment analysis of movie reviews using least squares twin support vector machine. In 1st Conference on Participles of Electrical and Computer Engineering. Payame Noor University, July 2017 ] [in Persian]


  • Nasiri, J. A., & Mir, A. M. (2020). An enhanced KNN-based twin support vector machine with stable learning rules. Neural Computing and Applications, 1-21.
  • Mir, A., & Nasiri, J. A. (2018). KNN-based least squares twin support vector machine for pattern classification. Applied Intelligence48(12), 4551-4564.
  • Mir, A. M., & Nasiri, J. A. (2019). LightTwinSVM: A Simple and Fast Implementation of Standard Twin Support Vector Machine Classifier. Journal of Open Source Software4, 1252.
  • Nasiri, J., Mir, A. M. I. R., & Fatahi, S. (2019). Classification of learning styles using behavioral features and twin support vector machine. Technology of Education Journal (TEJ) [in Persian]


  • Mir, A. M., Rahbar, M., & Nasiri, J. A. (2020). LIBTwinSVM: A Library for Twin Support Vector Machines. arXiv preprint arXiv:2001.10073.