Applications of Computational Intelligence: 6th IEEE Colombian Conference, ColCACI 2023, Bogota, Colombia, July 26-28, 2023, Revised Selected Papers

by Alvaro David Orjuela-Cañón (Editor), Jesus A Lopez (Editor), Julián David Arias-Londoño (Editor)

Overview

This book constitutes the refereed proceedings of the 6th IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023, held in Bogota, Colombia during July 2023.

A total of 11 full papers were carefully reviewed and selected from 42 submissions. They were organized in topical sections as follows: ​Biomedical Applications, Biological Applications, Engineering Applications.

Product Details

ISBN-13:	9783031484148
Publisher:	Springer Nature Switzerland
Publication date:	11/18/2023
Series:	Communications in Computer and Information Science , #1865
Edition description:	1st ed. 2024
Pages:	174
Product dimensions:	6.10(w) x 9.25(h) x (d)

Table of Contents

Biomedical Applications.- A Transfer Learning Scheme for COVID-19 Diagnosis from Chest X-Ray Images using Gradient-weighted Class Activation Mapping.- On the Comparison of Multilayer Perceptron and Extreme Learning Machine for Pedaling Recognition using EEG.- Self-supervised deep-learning segmentation of corneal endothelium specular microscopy images.- Tuberculosis Drug Discovery Estimation Process by Using Machine and Deep Learning Models.- Biological Applications.- A Robust Hybrid Control Approach tuned by PSO for Long-Time Delay Nonlinear Chemical Processes.- Feature selection of the spectral signature of cocoa bean based on visible and near-infrared spectroscopy for cadmium estimation.- On the use of Deep Learning Models for Automatic Animal Classification of Native Species in the Amazon.- Engineering Applications.- Intra-day Electricity Price Forecasting Based on a Time2Vec-LSTM Model.- Radio Frequency Pattern Matching – Subscriber Location in 5G Networks.- Robotic Arm based on Artificial Intelligence for Learning Braille as an Inclusive Tool in Educational Environments.- Spatial shrinkage prior: A probabilistic approach to model for categorical variables with many levels.