Sánchez-Gómez, Claudia
Main Affiliation
Preferred name
Sánchez-Gómez, Claudia
Official Name
Sánchez Gómez, Claudia Nallely
ORCID
0000-0001-6316-5214
Researcher ID
DTU-3417-2022
Scopus Author ID
57201851558
35 results
Now showing 1 - 10 of 35
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Item type:Publication, Generation of Hyperspectral Indices for Non-Invasive Crop Property Analysis Through Genetic Programming(IEEE, 2024-12-04)27 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Investment Portfolio Optimization Using Technical Indicators and White-Box ModelsQuantitative trading has revolutionized in recent years with the integration of machine learning. However, most proposals are complex models that often need help with model understanding and feature importance identification. This study presents a methodology for optimizing investment portfolios using the XGBoost algorithm and a comprehensive set of technical indicators. The primary objective is to maximize returns by accurately predicting stock prices and selecting the most profitable stocks. Our proposal is based on decision trees, eliminating the need for recurrent neural networks or time series representations of data and enabling white-box machine learning models that are easier to interpret. We tried our proposal with real data corresponding to a collection of stocks of the 500 most influential companies in the United States of America, utilizing historical data such as open prices, highest and lowest prices, and trading volume. Experimental results demonstrated that our approach successfully identified the most profitable stocks, outperforming random portfolios and showing significant profit accumulation over time. This approach recognizes the most feasible indicators and facilitates the automatic design of investment portfolios and the analysis of the importance of technical indicators in complex data.23 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Performance Comparison of Multi-Objective Optimizers for Dynamic Balancing of Six-Bar Watt Linkages Using a Fully Cartesian Model(MDPI AG, 2025-07-04); ; 9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 2 15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Physicochemical and Sensory Characteristics of Sausages Made with Grasshopper (Sphenarium purpurascens) Flour(2022); 1 5Scopus© Citations 49 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 1 13Scopus© Citations 4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Selecting crackling product based on sensory analysis by different statistical data approaches(2017); 1 11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Damage Importance Analysis for Pavement Condition Index Using Machine-Learning Sensitivity Analysis(2024); Jonás Velasco<jats:p>The Pavement Condition Index (PCI) is a prevalent metric for assessing the condition of rigid pavements. The PCI calculation involves evaluating 19 types of damage. This study aims to analyze how different types of damage impact the PCI calculation and the impact of the performance of prediction models of PCI by reducing the number of evaluated damages. The Municipality of León, Gto., Mexico, provided a dataset of 5271 records. We evaluated five different decision-tree models to predict the PCI value. The Extra Trees model, which exhibited the best performance, was used to assess the feature importance of each type of damage, revealing their relative impacts on PCI predictions. To explore the potential for reducing the complexity of the PCI evaluation, we applied Sequential Forward Search and Brute Force Search techniques to analyze the performance of models with various feature combinations. Our findings indicate no significant statistical difference in terms of Mean Absolute Error (MAE) and the coefficient of determination (R2) between models trained with 13 features compared to those trained with all 17 features. For instance, a model using only eight damages achieved an MAE of 4.35 and an R2 of 0.89, comparable to the 3.56 MAE and 0.92 R2 obtained with a model using all 17 features. These results suggest that omitting some damages from the PCI calculation has a minimal impact on prediction accuracy but can substantially reduce the evaluation’s time and cost. In addition, knowing the most significant damages opens up the possibility of automating the evaluation of PCI using artificial intelligence.</jats:p>1 12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Images dataset of beef meat samples with different shelf life(2023); ; ; 1 18 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, I3GO+ at RICATIM 2017: A semi-supervised approach to determine the relevance between images and text-annotations(2017)1 6Scopus© Citations 1