Výsledky vyhledávání - "Kebria, Parham M."

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Leveraging Optimal Transport for Enhanced Offline Reinforcement Learning in Surgical Robotic Environments

Autor: Zare, Maryam, Kebria, Parham M., Khosravi, Abbas

Most Reinforcement Learning (RL) methods are traditionally studied in an active learning setting, where agents directly interact with their environments, observe action outcomes, and learn through trial and error. However, allowing partially trained

Externí odkaz: http://arxiv.org/abs/2310.08841

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A Review of Machine Learning-based Security in Cloud Computing

Autor: Babaei, Aptin, Kebria, Parham M., Dalvand, Mohsen Moradi, Nahavandi, Saeid

Cloud Computing (CC) is revolutionizing the way IT resources are delivered to users, allowing them to access and manage their systems with increased cost-effectiveness and simplified infrastructure. However, with the growth of CC comes a host of secu

Externí odkaz: http://arxiv.org/abs/2309.04911

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A Review on Robot Manipulation Methods in Human-Robot Interactions

Autor: Zhang, Haoxu, Kebria, Parham M., Mohamed, Shady, Yu, Samson, Nahavandi, Saeid

Robot manipulation is an important part of human-robot interaction technology. However, traditional pre-programmed methods can only accomplish simple and repetitive tasks. To enable effective communication between robots and humans, and to predict an

Externí odkaz: http://arxiv.org/abs/2309.04687

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A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges

Autor: Zare, Maryam, Kebria, Parham M., Khosravi, Abbas, Nahavandi, Saeid

In recent years, the development of robotics and artificial intelligence (AI) systems has been nothing short of remarkable. As these systems continue to evolve, they are being utilized in increasingly complex and unstructured environments, such as au

Externí odkaz: http://arxiv.org/abs/2309.02473

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A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species Classification

Autor: Albardi, Feras, Kabir, H M Dipu, Bhuiyan, Md Mahbub Islam, Kebria, Parham M., Khosravi, Abbas, Nahavandi, Saeid

Publikováno v: 2021 IEEE International Conference on Systems, Man, and Cybernetics

This study aims to explore different pre-trained models offered in the Torchvision package which is available in the PyTorch library. And investigate their effectiveness on fine-grained images classification. Transfer Learning is an effective method

Externí odkaz: http://arxiv.org/abs/2110.07097

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An Uncertainty-aware Transfer Learning-based Framework for Covid-19 Diagnosis

Autor: Jokandan, Afshar Shamsi, Asgharnezhad, Hamzeh, Jokandan, Shirin Shamsi, Khosravi, Abbas, Kebria, Parham M., Nahavandi, Darius, Nahavandi, Saeid, Srinivasan, Dipti

The early and reliable detection of COVID-19 infected patients is essential to prevent and limit its outbreak. The PCR tests for COVID-19 detection are not available in many countries and also there are genuine concerns about their reliability and pe

Externí odkaz: http://arxiv.org/abs/2007.14846

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