Výsledky vyhledávání - "Pourmirzaei, Mahdi"

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A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Sites Prediction

Autor: Esmaili, Farzaneh, Pourmirzaei, Mahdi, Ramazi, Shahin, Shojaeilangari, Seyedehsamaneh, Yavari, Elham

Post-translational modifications (PTMs) have key roles in extending the functional diversity of proteins and as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM that

Externí odkaz: http://arxiv.org/abs/2208.04311

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Customizing an Affective Tutoring System Based on Facial Expression and Head Pose Estimation

Autor: Pourmirzaei, Mahdi, Montazer, Gholam Ali, Mousavi, Ebrahim

In recent years, the main problem in e-learning has shifted from analyzing content to personalization of learning environment by Intelligence Tutoring Systems (ITSs). Therefore, by designing personalized teaching models, learners are able to have a s

Externí odkaz: http://arxiv.org/abs/2111.14262

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A Brief Review of Machine Learning Techniques for Protein Phosphorylation Sites Prediction

Autor: Esmaili, Farzaneh, Pourmirzaei, Mahdi, Ramazi, Shahin, Yavari, Elham

Post-translational modifications (PTMs) have vital roles in extending the functional diversity of proteins and as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM tha

Externí odkaz: http://arxiv.org/abs/2108.04951

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How Self-Supervised Learning Can be Used for Fine-Grained Head Pose Estimation?

Autor: Pourmirzaei, Mahdi, Esmaili, Farzaneh, Mousavi, Ebrahim, Karamizadeh, Sasan, Shojaeilangari, Seyedehsamaneh

The cost of head pose labeling is the main challenge of improving the fine-grained Head Pose Estimation (HPE). Although Self-Supervised Learning (SSL) can be a solution to the lack of huge amounts of labeled data, its efficacy for fine-grained HPE is

Externí odkaz: http://arxiv.org/abs/2108.04893

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Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation

Autor: Pourmirzaei, Mahdi, Montazer, Gholam Ali, Esmaili, Farzaneh

In this paper, at first, the impact of ImageNet pre-training on fine-grained Facial Emotion Recognition (FER) is investigated which shows that when enough augmentations on images are applied, training from scratch provides better result than fine-tun

Externí odkaz: http://arxiv.org/abs/2105.06421

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