Zobrazeno 1 - 10
of 498
pro vyhledávání: '"Salih, Ahmed"'
Autor:
Salih, Ahmed M
Explainable Artificial Intelligence (XAI) emerged to reveal the internal mechanism of machine learning models and how the features affect the prediction outcome. Collinearity is one of the big issues that XAI methods face when identifying the most in
Externí odkaz:
http://arxiv.org/abs/2411.00846
Autor:
Salih, Ahmed M
Data pre-processing is a significant step in machine learning to improve the performance of the model and decreases the running time. This might include dealing with missing values, outliers detection and removing, data augmentation, dimensionality r
Externí odkaz:
http://arxiv.org/abs/2409.00155
Autor:
Salih, Ahmed M, Wang, Yuhe
Explainable artificial intelligence (XAI) is a set of tools and algorithms that applied or embedded to machine learning models to understand and interpret the models. They are recommended especially for complex or advanced models including deep neura
Externí odkaz:
http://arxiv.org/abs/2407.12177
Autor:
Salih, Ahmed M
Explainable Artificial Intelligence (XAI) methods help to understand the internal mechanism of machine learning models and how they reach a specific decision or made a specific action. The list of informative features is one of the most common output
Externí odkaz:
http://arxiv.org/abs/2406.11524
Autor:
Salih, Ahmed, Raisi-Estabragh, Zahra, Galazzo, Ilaria Boscolo, Radeva, Petia, Petersen, Steffen E., Menegaz, Gloria, Lekadir, Karim
eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning (ML) models into a more digestible form. These methods help to communicate how the model works with the aim of making ML models more transpare
Externí odkaz:
http://arxiv.org/abs/2305.02012
Autor:
Salih, Ahmed, Galazzo, Ilaria Boscolo, Raisi-Estabragh, Zahra, Petersen, Steffen E., Menegaz, Gloria, Radeva, Petia
Explainable Artificial Intelligence (XAI) provides tools to help understanding how the machine learning models work and reach a specific outcome. It helps to increase the interpretability of models and makes the models more trustworthy and transparen
Externí odkaz:
http://arxiv.org/abs/2304.01717
Autor:
Salih, Ahmed, Arif, Aksaan, Varadpande, Madhur, Fernandes, Rafael Tiza, Jankovic, Dragan, Kalasauskas, Darius, Ottenhausen, Malte, Kramer, Andreas, Ringel, Florian, Thavarajasingam, Santhosh G.
Publikováno v:
In eClinicalMedicine November 2024 77
Autor:
Hisham, Muhammed, Salih, Ahmed E., Shebeeb C, Muhammed, Catacutan, Mary Krystelle, Lee, Sungmun, Butt, Haider
Publikováno v:
In Cell Reports Physical Science 16 October 2024 5(10)
Autor:
Raisi-Estabragh, Zahra, Szabo, Liliana, McCracken, Celeste, Bülow, Robin, Aquaro, Giovanni Donato, Andre, Florian, Le, Thu-Thao, Suchá, Dominika, Condurache, Dorina-Gabriela, Salih, Ahmed M., Chadalavada, Sucharitha, Aung, Nay, Lee, Aaron Mark, Harvey, Nicholas C., Leiner, Tim, Chin, Calvin W.L., Friedrich, Matthias G., Barison, Andrea, Dörr, Marcus, Petersen, Steffen E.
Publikováno v:
In JACC: Cardiovascular Imaging July 2024 17(7):746-762
Autor:
Raisi-Estabragh, Zahra, Szabo, Liliana, Schuermans, Art, Salih, Ahmed M., Chin, Calvin W.L., Vágó, Hajnalka, Altmann, Andre, Ng, Fu Siong, Garg, Pankaj, Pavanello, Sofia, Marwick, Thomas H., Petersen, Steffen E.
Publikováno v:
In JACC: Cardiovascular Imaging May 2024 17(5):533-551