Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

This two-volume set LNCS 15368-15369 constitutes the refereed proceedings of the 27th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2024, held in Talca, Chile, during November 26-29, 2024.
The 35 full and 3 short papers presented in these proceedings were carefully reviewed and selected from 61 submissions. The papers presented in these two volumes are clustered into various thematical issues as follows:
Part I: Mathematical methods and computing techniques for artificial intelligence and pattern recognition, bioinformatics.
Part II: Biometrics, cognitive and humanoid vision, computer vision, image analysis, intelligent data analysis.

Inhalt

Unmasking Phishing Attempts A Study on Detection in Spanish Emails.- Comparative Analysis of Spatial and Spectral Methods in GNN for Power Flow in Electrical Power Systems.- An Effective Artificial Intelligence Pipeline for Automatic Manatee Count Using their Tonal Vocalizations.- Exploring Neural Joint Activity in Spiking Neural Networks for Fraud Detection.- Rethinking the Quality of Synthetic Palm Vein Images from Spectral Analysis.- An uncertainty driven ScaledYOLOv4 for open pit mining helmet detection.- A generative algorithm to compute NanoFingerprints.- Impact of agricultural production on climate change in South America comparative analysis between 1990 and 2020.- VAVnets retinal vasculature segmentation in few shot scenarios.- Remote Sensing Based Precipitation Detection using Conditional GAN and Recurrent Neural Networks.- Data Driven Genetic Algorithm for the Optimization of Water Distribution Networks A New Surrogate Model for Estimating Investment and Operational Costs in Pumping Stations.- Gene Regulatory Network for the Tryptophanase operon under the Threshold Boolean Network Model.- Multilabel Classification of Intracranial Hemorrhages using Deep Learning and Preprocessing Techniques on Non Contrast CT Images.- Segmentation of Brain Tumor Parts from Multi Spectral MRI Records Using Deep Learning and U net Architecture.- Exploiting the Segment Anything Model (SAM) for Lung Segmentation in Chest X ray Images.- Predicting Next Phases of Multi-Stage Network Attacks A Comparative Study of Statistical and Deep Learning Models.- Improving Suicide Ideation Screening with Machine Learning and Questionnaire Optimization through Feature Analysis.- Aquila Optimizer for hyperparameter metaheuristic optimization in Extreme Learning Machine.- Mixture of LSTM Experts for Sales Prediction with Diverse Features.