23rd Mexican International Conference on Artificial Intelligence, MICAI 2024, Tonantzintla, Mexico, October 21¿25, 2024, Proceedings, Part I Herausgegeben:Martínez-Villaseñor, Lourdes; Ochoa-Ruiz, Gilberto
23rd Mexican International Conference on Artificial Intelligence, MICAI 2024, Tonantzintla, Mexico, October 21¿25, 2024, Proceedings, Part I Herausgegeben:Martínez-Villaseñor, Lourdes; Ochoa-Ruiz, Gilberto
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.
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.
.- 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.
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