Zobrazeno 1 - 10
of 1 110
pro vyhledávání: '"A. Angona"'
Machine learning (ML) is a rapidly developing area of medicine that uses significant resources to apply computer science and statistics to medical issues. ML's proponents laud its capacity to handle vast, complicated, and erratic medical data. It's c
Externí odkaz:
http://arxiv.org/abs/2408.00348
Autor:
Rashid, Abdur, Biswas, Parag, Biswas, Angona, Nasim, MD Abdullah Al, Gupta, Kishor Datta, George, Roy
Artificial intelligence (AI) has become a crucial instrument for streamlining processes in various industries, including electrical power systems, as a result of recent digitalization. Algorithms for artificial intelligence are data-driven models tha
Externí odkaz:
http://arxiv.org/abs/2406.16965
Autor:
Biswas, Parag, Rashid, Abdur, Biswas, Angona, Nasim, Md Abdullah Al, Gupta, Kishor Datta, George, Roy
Reduced environmental effect, lower operating costs, and a stable and sustainable energy supply for current and future generations are the main reasons why power optimization is important. Power optimization makes ensuring that energy is used more ef
Externí odkaz:
http://arxiv.org/abs/2406.15732
Autor:
N. Estrada, B. Xicoy, M. Cabezón, S. Marcé, A. Senin, A. Angona, E. Alonso, M. Ratia, M. E. Plensa, J. Buch, X. Ortín, L. Zamora
Publikováno v:
HemaSphere, Vol 6, Pp 585-586 (2022)
Externí odkaz:
https://doaj.org/article/be2042d5e15b4f8c84ef12e2752afe5b
Autor:
X. Guerrero-Carreño, S. Smits, A. Esteban Lasso, J. A. Escudero García, M. Samiotaki, A. Alvarez-Larrán, A. Angona, A. J. Sáen Marín, V. García Gutiérrez, D. Dekkers, J. Demmers, C. M. Benavente Cuesta, F. Ferrer-Marín, J. C. Hernández-Boluda, F. J. Iborra, I. M. De Cuyper, P. Vandenberghe, P. Papadopoulos
Publikováno v:
HemaSphere, Vol 6, Pp 887-888 (2022)
Externí odkaz:
https://doaj.org/article/98b6de5fbdd4419db0454119a2d5b8be
Autor:
T. Barbui, A. Carobbio, A. Masciulli, A. Iurlo, M. A. Sobas, E. M. Elli, E. Rumi, V. De Stefano, F. Lunghi, M. Marchetti, R. Daffini, M. Gasior Kabat, B. Cuevas, M. L. Fox, M. M. Andrade Campos, F. Palandri, P. Guglielmelli, G. Benevolo, C. Harrison, M. A. Foncillas, M. Bonifacio, A. Alvarez-Larran, J.-J. Kiladjian, E. Bolanos Calderon, A. Patriarca, K. S. Quiroz Cervantes, M. Griesshammer, V. Garcia-Gutierrez, A. Marin Sanchez, E. Magro Mazo, M. Ruggeri, J. C. Hernandez-Boluda, S. Osorio, G. Carreno-Tarragona, M. Sagues Serrano, R. Kusec, B. Navas Elorza, A. Angona, B. Xicoy Cirici, E. Lopez Abadia, S. Koschmieder, E. Cichocka, A. Kulikowska de Nałęcz, M. Bellini, D. Cattaneo, C. Bucelli, F. Cavalca, O. Borsani, S. Betti, N. Curto-Garcia, S. Carbonell, L. Benajiba, A. Rambaldi, A. M. Vannucchi
Publikováno v:
HemaSphere, Vol 6, Pp 899-900 (2022)
Externí odkaz:
https://doaj.org/article/9986d19608d246e4b0f9027a154020f7
Autor:
Huda, Nafisa Labiba Ishrat, Biswas, Angona, Nasim, MD Abdullah Al, Rahman, Md. Fahim, Ahmed, Shoaib
Invariant scattering transform introduces new area of research that merges the signal processing with deep learning for computer vision. Nowadays, Deep Learning algorithms are able to solve a variety of problems in medical sector. Medical images are
Externí odkaz:
http://arxiv.org/abs/2307.04771
Autor:
Jidney, Tasmia Tahmida, Biswas, Angona, Nasim, MD Abdullah Al, Hossain, Ismail, Alam, Md Jahangir, Talukder, Sajedul, Hossain, Mofazzal, Ullah, Md Azim
The integration of machine learning in medical image analysis can greatly enhance the quality of healthcare provided by physicians. The combination of human expertise and computerized systems can result in improved diagnostic accuracy. An automated m
Externí odkaz:
http://arxiv.org/abs/2306.04750
Autor:
Sarker, Shuvra, Biswas, Angona, Nasim, MD Abdullah Al, Ali, Md Shahin, Puppala, Sai, Talukder, Sajedul
The field of medical imaging is an essential aspect of the medical sciences, involving various forms of radiation to capture images of the internal tissues and organs of the body. These images provide vital information for clinical diagnosis, and in
Externí odkaz:
http://arxiv.org/abs/2306.02055
Autor:
Biswas, Angona, Nasim, MD Abdullah Al, Imran, Al, Sejuty, Anika Tabassum, Fairooz, Fabliha, Puppala, Sai, Talukder, Sajedul
One way to expand the available dataset for training AI models in the medical field is through the use of Generative Adversarial Networks (GANs) for data augmentation. GANs work by employing a generator network to create new data samples that are the
Externí odkaz:
http://arxiv.org/abs/2306.02019