Advances in Computational Intelligence 23rd Mexican International Conference on Artificial Intelligence, MICAI 2024, Tonantzintla, Mexico, October 21–25, 2024, Proceedings, Part I

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.

.- 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.