Special Issue in Artificial Intelligence
Special Issue in Artificial Intelligence
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Citación
Date
2019-11-12
Author
Ballesteros, Dora Maria
Publisher
Universidad Militar Nueva Granada
Key words
Metadata
Show full item recordResumen
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].
Abstract
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].
Enlace al recurso
https://revistas.unimilitar.edu.co/index.php/rcin/article/view/4354https://doi.org/10.18359/rcin.4354
Fuente
Ciencia e Ingenieria Neogranadina; Vol. 30 No. 1 (2020); 7-8Ciencia e Ingeniería Neogranadina; Vol. 30 Núm. 1 (2020); 7-8
Ciencia e Ingeniería Neogranadina; v. 30 n. 1 (2020); 7-8