Full Description
The two-volume set, LNAI 15246 and 15247, constitutes the proceedings of the 23rd Mexican International Conference on Artificial Intelligence, MICAI 2024, held in Tonantzintla, Mexico in October 21-25, 2024.
The 37 full papers presented in these proceedings were carefully reviewed and selected from 141 submissions. The papers presented in these two volumes are organized in the following topical sections:
Part I - Machine Learning; Computer Vision.
Part II - Intelligent Systems; Bioinformatics and Medical Applications; Natural Language Processing.
Contents
.- Machine Learning.
.- Towards Estimating Water Consumption in Semi-Arid Urban Landscaping: A Machine Learning Approach.
.- Talent Identification in Football Using Supervised Machine Learning.
.- Latent State Space Quantization for Learning and Exploring Goals.
.- Predicting and Classifying Contaminants in Mexican Water Bodies.
.- A ConvLSTM approach for the WorldClim Dataset in Mexico.
.- Building Resilience Against Climate Change, Focusing on Predicting Precipitation with Machine Learning Models on Mexico's Metropolitan Area.
.- Machine Learning Approaches for Water Quality Monitoring in the Desert State of Sonora.
.- Predicting Water Levels Using Gradient Boosting Regressor and LSTM Models: A Case Study of Lago de Chapala Dam.
.- Efficiently Mining High Average Utility Co-location Patterns Using Maximal Cliques and Pruning Strategies.
.- QUE MAX-TE-LATTE Personalized Product Recommendations in the ' Coffee Shop Industry: Enhancing Customer Experience and Loyalty.
.- Price Estimation for Pre-Owned Vehicles Using Machine Learning.
.- Algotrading R2ED: A Machine Learning Approach.
.- Analysis of Predictive Factors in University Dropout Rates Using Data Science Techniques.
.- Machine Learning.
.- Incremental learning for object classification in a real and dynamic world.
.- Easy for us, complex for AI: Assessing the coherence of generated realistic images.
.- Comparative analysis of natural landmark detection in lunar terrain images.
.- Exploring Anchor-Free Object Detection Models for Surgical Tool Detection: A Comparative Study of Faster-RCNN, YOLOv4, and CenterNet++.
.- Smartphone-based Fuel Identification Model for Wildifire Risk Assessment using YOLOv8.
