Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Pablo Guillen-Rondon"'
Autor:
Harold Brayan Arteaga-Arteaga, Alejandro Mora-Rubio, Frank Florez, Nicolas Murcia-Orjuela, Cristhian Eduardo Diaz-Ortega, Simon Orozco-Arias, Melissa delaPava, Mario Alejandro Bravo-Ortíz, Melvin Robinson, Pablo Guillen-Rondon, Reinel Tabares-Soto
Publikováno v:
PeerJ Computer Science, Vol 7, p e798 (2021)
Recent advances in artificial intelligence with traditional machine learning algorithms and deep learning architectures solve complex classification problems. This work presents the performance of different artificial intelligence models to classify
Externí odkaz:
https://doaj.org/article/e1d8468b25c0456492404d1fa7d1a9e8
Autor:
Harold Brayan Arteaga-Arteaga, Mariana S Candamil-Cortés, Brian Breaux, Pablo Guillen-Rondon, Simon Orozco-Arias, Reinel Tabares-Soto
Artificial Intelligence (AI) is revolutionizing all fields that affect people's lives and health. One of the most critical applications is in the study of tumors. It is the case of glioblastoma (GBM) that has behaviors that need to be understood to d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fe9d83822b478212c2d3014cbece2edc
Autor:
Pablo Guillen Rondon
Publikováno v:
2020 CIEC Proceedings.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b235d7892fde833e3546392796333f46
https://doi.org/10.18260/1-2-510-38729
https://doi.org/10.18260/1-2-510-38729
Autor:
Frank Florez, Melvin Robinson, Simon Orozco-Arias, Reinel Tabares-Soto, Pablo Guillen-Rondon, Alejandro Mora-Rubio, Nicolas Murcia-Orjuela, Cristhian Eduardo Diaz-Ortega, Mario Alejandro Bravo-Ortiz, Melissa delaPava, Harold Brayan Arteaga-Arteaga
Publikováno v:
PeerJ Computer Science, Vol 7, p e798 (2021)
PeerJ Computer Science
PeerJ Computer Science
Recent advances in artificial intelligence with traditional machine learning algorithms and deep learning architectures solve complex classification problems. This work presents the performance of different artificial intelligence models to classify
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39a952069e8985f0353b51ffec3546c2
https://peerj.com/articles/cs-798.pdf
https://peerj.com/articles/cs-798.pdf
Publikováno v:
EATIS
Recent advances in single-cell RNA sequencing technologies enable deep insights into cellular development, gene regulation, and phenotypic diversity by measuring gene expression for thousands of cells in a single experiment. This results in high-thro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::522e4a4577353669542c12fdc2c5a936
https://doi.org/10.1145/3401895.3402077
https://doi.org/10.1145/3401895.3402077
Publikováno v:
SEG Technical Program Expanded Abstracts 2019.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::12e4dd57a47ca3e285a9715f837620d9
https://doi.org/10.1190/segam2019-3216712.1
https://doi.org/10.1190/segam2019-3216712.1
Publikováno v:
Communications in Computer and Information Science ISBN: 9783030162047
CARLA
CARLA
Breast cancer is the second leading cause of cancer death among women. Breast cancer is not a single disease, but rather is comprised of many different biological entities with distinct pathological features and clinical implications. Pathologists fa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::70658690a32b095369df33edc42e2ec4
https://doi.org/10.1007/978-3-030-16205-4_3
https://doi.org/10.1007/978-3-030-16205-4_3
Publikováno v:
2018 4th International Conference on Big Data and Information Analytics (BigDIA).
Breast cancer is the second leading cause of cancer death among women. Breast cancer is not a single disease, but rather is comprised of many different biological entities with distinct pathological features and clinical implications. Pathologists fa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::445a43f7f0489ecde22c0addd24e4b33
https://doi.org/10.1109/bigdia.2018.8632800
https://doi.org/10.1109/bigdia.2018.8632800
Autor:
Pablo Guillen-Rondon, Melvin Robinson
Publikováno v:
2016 International Conference on Computational Science and Computational Intelligence (CSCI).
An approach to modeling complex real-world data such as biomedical signals is to develop pattern recognition techniques and robust features that capture the relevant information. In this paper, we use a deep belief network (DBN) to predict subcortica
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a3c7bf87813e2113be0f6e61332d650d
https://doi.org/10.1109/csci.2016.0036
https://doi.org/10.1109/csci.2016.0036