| dc.creator | Ballesteros, Dora Maria | |
| dc.date | 2019-11-12 | |
| dc.identifier | https://revistas.unimilitar.edu.co/index.php/rcin/article/view/4354 | |
| dc.identifier | 10.18359/rcin.4354 | |
| dc.description | Artificial intelligence (AI) is an interdisciplinary subject in science and engineering that makes it possible for machines to learn from data. Artificial Intelligence applications include prediction, recommendation, classification and recognition, object detection, natural language processing, autonomous systems, among others. The topics of the articles in this special issue include deep learning applied to medicine [1, 3], support vector machine applied to ecosystems [2], human-robot interaction [4], clustering in the identification of anomalous patterns in communication networks [5], expert systems for the simulation of natural disaster scenarios [6], real-time algorithms of artificial intelligence [7] and big data analytics for natural disasters [8]. | en-US |
| dc.description | Artificial intelligence (AI) is an interdisciplinary subject in science and engineering that makes it possible for machines to learn from data. Artificial Intelligence applications include prediction, recommendation, classification and recognition, object detection, natural language processing, autonomous systems, among others. The topics of the articles in this special issue include deep learning applied to medicine [1, 3], support vector machine applied to ecosystems [2], human-robot interaction [4], clustering in the identification of anomalous patterns in communication networks [5], expert systems for the simulation of natural disaster scenarios [6], real-time algorithms of artificial intelligence [7] and big data analytics for natural disasters [8]. | es-ES |
| dc.format | application/pdf | |
| dc.format | text/xml | |
| dc.language | spa | |
| dc.language | eng | |
| dc.publisher | Universidad Militar Nueva Granada | es-ES |
| dc.relation | https://revistas.unimilitar.edu.co/index.php/rcin/article/view/4354/4077 | |
| dc.relation | https://revistas.unimilitar.edu.co/index.php/rcin/article/view/4354/4246 | |
| dc.relation | /*ref*/Perdomo Charry, O. J., & González Osorio, F. A. (2019). A Systematic Review of Deep Learning Methods Applied to Ocular Images. Ciencia E Ingenieria Neogranadina, 30(1). https://doi.org/10.18359/rcin.4242 | |
| dc.relation | /*ref*/Martin, L. D., Medina, J., & Upegui, E. (2019). Assessment of Image-Texture Improvement Applied to Unmanned Aerial Vehicle Imagery for the Identification of Biotic Stress in Espeletia. Case Study: Moorlands of Chingaza (Colombia). Ciencia E Ingenieria Neogranadina, 30(1). https://doi.org/10.18359/rcin.3842 | |
| dc.relation | /*ref*/Castillo, J. A., Granados, Y. C., & Fajardo Ariza, C. A. (2019). Patient-Specific Detection of Atrial Fibrillation in Segments of ECG Signals using Deep Neural Networks. Ciencia E Ingenieria Neogranadina, 30(1). https://doi.org/10.18359/rcin.4156 | |
| dc.relation | /*ref*/Muñoz Peña, K., & Bacca Cortes, B. (2019). GUI3DXBot: An Interactive Software Tool for a Tour-Guide Mobile Robot. Ciencia E Ingenieria Neogranadina, 30(1). https://doi.org/10.18359/rcin.3644 | |
| dc.relation | /*ref*/Leal Piedrahita, E. A. (2019). Hierarchical Clustering for Anomalous Traffic Conditions Detection in Power Substations. Ciencia E Ingenieria Neogranadina, 30(1). https://doi.org/10.18359/rcin.4236 | |
| dc.relation | /*ref*/Florez Zuluaga, J. A., Patino Carrasco, E., Ortega Pabon, J. D., Gallego Leon, K., & Quintero Montoya, O. L. (2019). A Data Fusion System for Simulation of Critical Scenarios and Decision-Making. Ciencia E Ingenieria Neogranadina, 30(1). https://doi.org/10.18359/rcin.4131 | |
| dc.relation | /*ref*/González, E., Villamizar Luna, W. D., & Fajardo Ariza, C. A. (2019). A Hardware Accelerator for the Inference of a Convolutional Neural network. Ciencia E Ingenieria Neogranadina, 30(1). https://doi.org/10.18359/rcin.4194 | |
| dc.relation | /*ref*/Martínez Quezada, D. O., Sierra Robinson, R., Martínez Cano, J. G., & Lamos Díaz, H. (2019). Stakeholders Identification in a Disaster Through Twitter: Study Case SINABUNG 2018. Ciencia E Ingenieria Neogranadina, 30(1). https://doi.org/10.18359/rcin.3938 | |
| dc.rights | Derechos de autor 2019 Ciencia e Ingeniería Neogranadina | es-ES |
| dc.source | Ciencia e Ingenieria Neogranadina; Vol. 30 No. 1 (2020); 7-8 | en-US |
| dc.source | Ciencia e Ingeniería Neogranadina; Vol. 30 Núm. 1 (2020); 7-8 | es-ES |
| dc.source | Ciencia e Ingeniería Neogranadina; v. 30 n. 1 (2020); 7-8 | pt-BR |
| dc.source | 1909-7735 | |
| dc.source | 0124-8170 | |
| dc.subject | Deep learning | en-US |
| dc.subject | Support Vector Machines | en-US |
| dc.subject | Clustering | en-US |
| dc.subject | Expert systems | en-US |
| dc.subject | Big data analytics | en-US |
| dc.title | Special Issue in Artificial Intelligence | en-US |
| dc.title | Special Issue in Artificial Intelligence | es-ES |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:eu-repo/semantics/publishedVersion | |
| dc.type | texto | en-US |
