Sánchez-Gómez, Claudia
Thumbnail Image
Preferred name
Sánchez-Gómez, Claudia
Official Name
Sánchez Gómez, Claudia Nallely
Alternative Name
cnsanchez
Main Affiliation
ORCID
Scopus Author ID
57201851558
Researcher ID
DTU-3417-2022

Research Output
Now showing 1 - 10 of 32
No Thumbnail Available
Publication

Modeling of Amorphous-Carbon Cells for Molecular Dynamics Simulations

2019 , Sánchez-Gómez, Claudia , Domínguez-Soberanes, Julieta , ORTIZ-MEDINA, JOSUE

View
No Thumbnail Available
Publication

Night club recommendation system based on decision trees

2021 , Sánchez-Gómez, Claudia , Jose-Carlos Delgado-Gomez , Pablo Ramirez-Espana , Luis Garcia-Zermeno , Samantha Licea , Domínguez-Soberanes, Julieta

View
No Thumbnail Available
Publication

Sustainability of Urban Parks: Applicable Methodological Framework for a Simple Assessment

2023 , Teresa González-ramírez , Berger, Pia , Sánchez-Gómez, Claudia , Faezeh Mahichi

Urban parks are central to advancing urban sustainability and improving overall quality of life by providing green spaces that promote physical and mental well-being, mitigate environmental issues, and foster community cohesion. However, there is a lack of methodologies that measure these benefits and provide a sustainability rating. In this study, we propose a valuable tool for measuring the sustainability level of urban parks: low (0–50%), medium (51–79%), and high (80–100%). It employs effective and affordable measures for the daily management of urban parks. It is rooted in the three pillars of sustainability: environmental, social, and economic. We have defined 19 indicators (e.g., renewable energy and energy efficiency, environmental impact on society) and 50 criteria (e.g., clean energy generation, water workshops). A multi-criteria analysis facilitated the selection process for these indicators and criteria. This methodology is developed by characterizing and systematically documenting the park’s day-to-day operations. We present a case study of Cárcamos Park in Guanajuato, Mexico. Through this real-life scenario, we demonstrate our methodology’s high applicability and effectiveness. The sustainability assessment of Cárcamos Park reveals a level of 57%, with the environmental pillar at 47.7%, the economic pillar at 49%, and the social pillar at 75%. The adaptability of our methodology during the design phase of new parks plays a crucial role in shaping sustainable park layouts. Park managers can apply our procedure to any park, evaluate their sustainability status, and detect areas of opportunity.

View
No Thumbnail Available
Publication

Liking Product Landscape: Going Deeper into Understanding Consumers’ Hedonic Evaluations

2019 , Sánchez-Gómez, Claudia , Domínguez-Soberanes, Julieta , Héctor B. Escalona-Buendía , Mario Graff , Gutiérrez, Sebastián , Gabriela Sánchez

The use of graphical mapping for understanding the comparison of products based on consumers’ perceptions is beneficial and easy to interpret. Internal preference mapping (IPM) and landscape segmentation analysis (LSA) have successfully been used for this propose. However, including all the consumers’ evaluations in one map, with products’ overall liking and attributes’ perceptions, is complicated; because data is in a high dimensional space some information can be lost. To provide as much information as possible, we propose the liking product landscape (LPL) methodology where several maps are used for representing the consumers’ distribution and evaluations. LPL shows the consumers’ distribution, like LSA, and also it superimposes the consumers’ evaluations. However, instead of superimposing the average overall liking in one map, this methodology uses different maps for each consumer’s evaluation. Two experiments were performed where LPL was used for understanding the consumers’ perceptions and compared with classic methodologies, IPM and cluster analysis, in order to validate the results. LPL can be successfully used for identifying consumers’ segments, consumers’ preferences, recognizing perception of product attributes by consumers’ segments and identifying the attributes that need to be optimized.

View
No Thumbnail Available
Publication

Damage Importance Analysis for Pavement Condition Index Using Machine-Learning Sensitivity Analysis

2024 , Alejandro Pérez Carvajal , Sánchez-Gómez, Claudia , Jonás Velasco

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.

View
No Thumbnail Available
Publication

Analysis of wind missing data for wind farms in Isthmus of Tehuantepec

2018 , Sánchez-Gómez, Claudia , J. Enriquez-Zarate , Velázquez, Ramiro , Mario Graff , S. Sassi

View
No Thumbnail Available
Publication

Gradient Descent-Based Optimization Method of a Four-Bar Mechanism Using Fully Cartesian Coordinates

2019 , Orvañanos-Guerrero, María T. , Sánchez-Gómez, Claudia , Mariano Rivera , Acevedo, Mario , Velázquez, Ramiro

Machine vibrations often occur due to dynamic unbalance inducing wear, fatigue, and noise that limit the potential of many machines. Dynamic balancing is a main concern in mechanism and machine theory as it allows designers to limit the transmission of vibrations to the frames and base of machines. This work introduces a novel method for representing a four-bar mechanism with the use of Fully Cartesian coordinates and a simple definition of the shaking force (ShF) and the shaking moment (ShM) equations. A simplified version of Projected Gradient Descent is used to minimize the ShF and ShM functions with the aim of balancing the system. The multi-objective optimization problem was solved using a linear combination of the objectives. A comprehensive analysis of the partial derivatives, volumes, and relations between area and thickness of the counterweights is used to define whether the allowed optimization boundaries should be changed in case the mechanical conditions of the mechanism permit it. A comparison between Pareto fronts is used to determine the impact that each counterweight has on the mechanism’s balancing. In this way, it is possible to determine which counterweights can be eliminated according to the importance of the static balance (ShF), dynamic balance (ShM), or both. The results of this methodology when using three counterweights reduces the ShF and ShM by 99.70% and 28.69%, respectively when importance is given to the static balancing and by 83.99% and 8.47%, respectively, when importance is focused on dynamic balancing. Even when further reducing the number of counterweights, the ShF and ShM can be decreased satisfactorily.

View
No Thumbnail Available
Publication

A comparative analysis of finite element programs for their implementation in a graphic tool of insulated power cables

2017 , Gutiérrez, Sebastián , Sánchez-Gómez, Claudia , Jeronimo Alvarez , Domínguez-Soberanes, Julieta

View
No Thumbnail Available
Publication

Convolutional neural networks for detection of hand-written drawings

2020 , Valenzuela, Sergio , Calabrese, Bernardo , ORTIZ-MEDINA, JOSUE , Sánchez-Gómez, Claudia

View
No Thumbnail Available
Publication

Recommendation System for a Delivery Food Application Based on Number of Orders

2023 , Sánchez-Gómez, Claudia , Domínguez-Soberanes, Julieta , Alejandra Arreola , Mario Graff

With the recent growth in food-delivery applications, creating new recommendation systems tailored to this platform is essential. State-of-the-art restaurant recommendation systems are based on users’ ratings or reviews, with data that are obtained from questionnaires or online platforms such as TripAdvisor, Zomato, Foursquare, or Yield. However, not all users give ratings or reviews after their purchase. This document proposes a recommendation system whose input is the number of orders stored by a real food-delivery application. These data are always available for all food-delivery applications and are stored all the time. Our proposal is based on the nearest-neighbor technique that calculates the client’s preferred restaurants and analyzes other clients with similar buying patterns. In addition, we propose a performance metric that can be used for this specific recommendation system that is based on real restaurant sales. We use a real dataset (available online) to validate our proposal. Based on our experiments, the recommendation system successfully gives only an average of 7.7 options from 187 that are available. We compared our proposal with other state-of-the-art recommendation techniques and obtained a better performance. Our results indicate that it is possible to generate recommendations based on the number of orders, making the use of a restaurant-recommendation system feasible in a real food-delivery application.

View