Applications of Computational Intelligence : 6th IEEE Colombian Conference, ColCACI 2023, Bogota, Colombia, July 26-28, 2023, Revised Selected Papers (Communications in Computer and Information Science)

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Applications of Computational Intelligence : 6th IEEE Colombian Conference, ColCACI 2023, Bogota, Colombia, July 26-28, 2023, Revised Selected Papers (Communications in Computer and Information Science)

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  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 174 p.
  • 商品コード 9783031484148

Full Description

​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. 

 

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.          

    

       

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