TY  - BOOK
AU  - Orjuela-Cañón,Alvaro David
AU  - Lopez,Jesus
AU  - Arias-Londoño,Julian David
AU  - Figueroa-García,Juan Carlos
ED  - SpringerLink (Online service)
TI  - Applications of Computational Intelligence: 5th IEEE Colombian Conference, ColCACI 2022, Cali, Colombia, July 27-29, 2022, Revised Selected Papers 
T2  - Communications in Computer and Information Science,
SN  - 9783031297830
AV  - Q334-342 
U1  - 006.3 23
PY  - 2023///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Artificial intelligence
KW  - Computer science
KW  - Mathematics
KW  - Computers, Special purpose
KW  - Algorithms
KW  - Education
KW  - Data processing
KW  - Artificial Intelligence
KW  - Mathematics of Computing
KW  - Special Purpose and Application-Based Systems
KW  - Design and Analysis of Algorithms
KW  - Computers and Education
N1  - Acceso multiusuario; Design of a segmentation and classification system for seed detection based on pixel intensity thresholds and convolutional neural networks -- Classification of Focused Perturbations Using Time-Variant Functional Connectivity with rs-fmri -- Escherichia coli: Analysis of Features for Protein Localization Classification employing Fusion Data -- Artificial Bee Colony-Based Dynamic Sliding Mode Controller for Integrating Processes with Inverse Response and Deadtime -- Optimizing a Dynamic Sliding Mode Controller with Bio-Inspired Methods: A Comparison -- A robust controller based on LAMDA and Smith Predictor applied to a system with dominant time delay -- Recursive neural networks tuned with a genetic algorithm for the prediction of the Bancolombia stock
N2  - This book constitutes the refereed proceedings of the 5th IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2022, held in Cali, Colombia during July 27-29, 2022. The 7 extended papers included in this book were carefully reviewed and selected from 38 submissions. They were organized in topical sections as follows: Design of a segmentation and classification system for seed detection based on pixel intensity thresholds and convolutional neural networks
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-29783-0
ER  -