Zobrazeno 1 - 10
of 247
pro vyhledávání: '"Yesha, Yelena"'
Venous thromboembolism (VTE) is a critical cardiovascular condition, encompassing deep vein thrombosis (DVT) and pulmonary embolism (PE). Accurate and timely identification of VTE is essential for effective medical care. This study builds upon our pr
Externí odkaz:
http://arxiv.org/abs/2408.09043
Distributional drift detection is important in medical applications as it helps ensure the accuracy and reliability of models by identifying changes in the underlying data distribution that could affect diagnostic or treatment decisions. However, cur
Externí odkaz:
http://arxiv.org/abs/2408.08456
Internet of Things (IoT) services necessitate the storage, transmission, and analysis of diverse data for inference, autonomy, and control. Blockchains, with their inherent properties of decentralization and security, offer efficient database solutio
Externí odkaz:
http://arxiv.org/abs/2405.04803
Autor:
Deng, Jamie, Wu, Yusen, Hayssen, Hilary, Englum, Brain, Kankaria, Aman, Mayorga-Carlin, Minerva, Sahoo, Shalini, Sorkin, John, Lal, Brajesh, Yesha, Yelena, Nguyen, Phuong
Publikováno v:
2023 International Conference on Bioinformatics and Biomedicine (BIBM)
Rapid and accurate identification of Venous thromboembolism (VTE), a severe cardiovascular condition including deep vein thrombosis (DVT) and pulmonary embolism (PE), is important for effective treatment. Leveraging Natural Language Processing (NLP)
Externí odkaz:
http://arxiv.org/abs/2309.12273
Federated Learning (FL) has revolutionized how we train deep neural networks by enabling decentralized collaboration while safeguarding sensitive data and improving model performance. However, FL faces two crucial challenges: the diverse nature of da
Externí odkaz:
http://arxiv.org/abs/2309.12267
Stochastic Gradient Descent (SGD), a widely used optimization algorithm in deep learning, is often limited to converging to local optima due to the non-convex nature of the problem. Leveraging these local optima to improve model performance remains a
Externí odkaz:
http://arxiv.org/abs/2309.12259
Autor:
Menon, Sumeet, Mangalagiri, Jayalakshmi, Galita, Josh, Morris, Michael, Saboury, Babak, Yesha, Yaacov, Yesha, Yelena, Nguyen, Phuong, Gangopadhyay, Aryya, Chapman, David
We present a novel algorithm that is able to classify COVID-19 pneumonia from CT Scan slices using a very small sample of training images exhibiting COVID-19 pneumonia in tandem with a larger number of normal images. This algorithm is able to achieve
Externí odkaz:
http://arxiv.org/abs/2110.01605
Adversarial attacks attempt to disrupt the training, retraining and utilizing of artificial intelligence and machine learning models in large-scale distributed machine learning systems. This causes security risks on its prediction outcome. For exampl
Externí odkaz:
http://arxiv.org/abs/2109.02018
Autor:
Khodadadi, Nima, Roghani, Hossein, De Caso, Francisco, El-kenawy, El-Sayed M., Yesha, Yelena, Nanni, Antonio
Publikováno v:
In Thin-Walled Structures May 2024 198
Autor:
Hayssen, Hilary, Sahoo, Shalini, Nguyen, Phuong, Mayorga-Carlin, Minerva, Siddiqui, Tariq, Englum, Brian, Slejko, Julia F., Mullins, C. Daniel, Yesha, Yelena, Sorkin, John D., Lal, Brajesh K.
Publikováno v:
In Journal of Vascular Surgery: Venous and Lymphatic Disorders March 2024 12(2)