Editor


Sima Ranjbari Wayne State University, United States
Email: sima.ranjbari@wayne.edu


Sima Ranjbari

Shahsavari, A., Ranjbari, S., & Khatibi, T. (2021). Proposing a novel Cascade Ensemble Super Resolution Generative Adversarial Network (CESR-GAN) method for the reconstruction of super-resolution skin lesion images. Informatics in Medicine Unlocked, 24, 100628.

Musha, A., Al Mamun, A., Tahabilder, A., Hossen, M., Jahan, B., & Ranjbari, S. (2022). A deep learning approach for COVID-19 and pneumonia detection from chest X-ray images. International Journal of Electrical & Computer Engineering (2088-8708), 12(4).

Ranjbari, S., Khatibi, T., Vosough Dizaji, A., Sajadi, H., Totonchi, M., & Ghaffari, F. (2021). CNFE-SE: a novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of intrauterine insemination and ranking the features. BMC Medical Informatics and Decision Making, 21(1), 1-29.

Ranjbari, S., Khatibi, T., Dizaj, A. V. T., Sajadi, H., Totonchi, M., & Ghaffari, F. (2020). CNFE-SE: A novel hybrid approach combining complex network-based feature engineering and stacked ensemble to predict the success of Intrauterine Insemination and ranking the features (Doctoral dissertation, Royan Institute).

Shahsavari, A., Khatibi, T., & Ranjbari, S. (2022). Skin lesion detection using an ensemble of deep models: SLDED. Multimedia Tools and Applications, 1-20.

Shahsavari, A., Ranjbari, S., & Khatibi, T. (2021). Proposing a novel Cascade Ensemble Super Resolution Generative Adversarial Network (CESR-GAN) method for the reconstruction of super-resolution skin lesion images. Informatics in Medicine Unlocked, 24, 100628.