CRIS

Permanent URI for this communityhttps://scripta.up.edu.mx/handle/20.500.12552/1

Browse

Search Results

Now showing 1 - 10 of 38

Some of the metrics are blocked by your
consent settings
Predicting Substance Addiction in University Students: A DSM-5-Guided Machine Learning Model
(Springer Nature Switzerland, 2025-10-24)
González Bustamante, Pablo
;
Substance use among university students is a growing health concern that is often overlooked until it escalates into a full-grown disorder. This study presents a multiclass machine learning model for predicting substance use risk levels based on DSM-5 diagnostic criteria and psychosocial factors such as trauma, academic stress and social networks. Data were collected through a survey answered by university students, the resulting dataset was used to train and compare multiple models. After performing feature selection, class balancing and hyperparameter tuning, the best performing and most accurate model, was a logistic-regression model that achieved a macro F1-score of 0.946. More notably however, the model showed improved sensitivity for mild-risk cases, which tend to go underdetected in binary classification schemes. These results support the integration of clinically based machine learning models, into educational institutions health protocols. ©The authors ©Springer.
Some of the metrics are blocked by your
consent settings
Making Better Medical Decisions Using Machine Learning: A Bayesian Model
(Springer Nature Switzerland, 2025)
;
Currently, most countries seek universal health coverage for all people; however, in the face of the crisis in health systems caused by the COVID-19 pandemic and the high demand for these services, it is more relevant to have tools that allow faster data taking. However, making medical decisions is one of the most complex processes. Artificial intelligence (AI) is a rapidly evolving field that can transform various aspects of healthcare, such as diagnosis, treatment, prevention, and management. However, to have confidence in the systems, the actors must ensure they are adequately trained to make correct decisions. This research analyzes medical decision-making through Bayesian networks with machine learning techniques. This research creates a methodology and model for making medical decisions based on artificial intelligence. The model shows critical factors that optimally influence decision-making to generate value that translates into patient health. The results show that optimal or non-optimal medical decision-making and its various aspects through the causality of the variables allow the interrelation to be more adequately captured to manage it. The most relevant factors for adequate decision-making are Ethical Issues, Risk/Benefit, Scientific Integrity, Transparent Decisions, Data Preprocessing and Curation, Performance Evaluation, and ML Model. ©The authors © Springer.
Some of the metrics are blocked by your
consent settings
Regulations and Laws Affecting Women’s Economic Opportunities: A Worldwide Approach
(SAGE Publications, 2025)
Hernández-Lara, Ana Beatriz
;
;
;
Leyva-Hernández, Sandra Nelly
This research aims to analyze the regulations and laws that promote economic opportunities for women at an international level, predict their impact on income levels, and estimate when legal gender equality will be achieved across different regions. The countries are compared over time, based on their income levels and regional locations, considering regulatory indicators on mobility, workplace, pay, marriage, parenthood, entrepreneurship, assets, and pensions. The methodological strategy was based on machine learning methods. The results indicate a positive trend in the average scores of all regulatory indicators, revealing significant differences across groups of countries and suggesting more egalitarian regulatory frameworks for developed countries, as well as more imbalanced and less progressive frameworks for underdeveloped and developing countries. The regulatory axes that better predict a country’s income level were parenthood, analyzing laws affecting women’s work after having children; assets, which consider gender differences in ownership and inheritance; and marriage, related to the legal constraints on women affected by marriage and divorce. However, the paternity axis is the last to be achieved. ©The authors ©SAGE Publications ©SAGE Open.
Some of the metrics are blocked by your
consent settings
Management of scientific and ancestral knowledge: a decision-making model in mezcal industry in Mexico
(Frontiers Media SA, 2025)
;
;
Leyva-Hernández, Sandra Nelly
Introduction: Knowledge management is essential to ensure the sustainability of rural communities and small producers since it generates value for innovation, productivity, and competitiveness. The aim of this study is to identify relevant factors for adequate decision-making in managing knowledge in the Mexican mezcal industry and its impact on developing rural communities and small producers - mezcaleros. For this purpose, a decision-making model for managing scientific and ancestral knowledge is created to support links with universities, research centers, and rural communities to accelerate innovation and competitiveness in this sector. Methods: The analysis methods were carried out through decision-making, machine-learning techniques, and fuzzy logic. Results: The Bayesian Network model suggests that the preceding variables to optimize the Mezcaleros Knowledge Management are the Mezcaleros Indigenous community, the Denomination of Origin, Scientific and Ancestral Knowledge, Waste Management and Use, and Jima. Discussion: This knowledge management model aims to guide small producers to be more productive and competitive through the support of a facilitator. ©The authors ©Frontiers in Artificial Intelligence ©Frontiers Media SA.
Some of the metrics are blocked by your
consent settings
Modeling the Relation Between Non-Communicable Diseases and the Health Habits of the Mexican Working Population: A Hybrid Modeling Approach
(MDPI AG, 2025)
Domínguez-Miranda, Sergio Arturo
;
;
The impact that Non-Communicable Diseases (NCDs) have on the health status of the population has generated the need for an in-depth analysis of health habits and NCDs. In addition to its significant impact on population health, this phenomenon also translates into substantial economic consequences for countries. This study delves into the analysis of the relationship between health habits and NCDs among the economically active population of Mexico. Through a hybrid approach that integrates the use of machine learning (ML) models and a structural equation model (SEM), we seek to quantify the direct and indirect causal effects between health habits and NCDs. For this study, information from the 2022 National Health and Nutrition Survey carried out in Mexico for the working-age population is used. According to the results obtained in the first stage of analysis using ML, the most relevant variables (health habits) that impact the probability of individuals presenting with NCDs were identified (random forest precision of 78.66% and Lasso with 71.27%). The second stage of analysis through SEM using the most relevant variables, which were selected through ML, allowed us to measure the direct and indirect causal effect of health habits on NCDs. The SEM model was statistically significant (Chi-square: 449.186; p-value = 0.0000) and revealed that negative health habits, such as a poor diet, physical inactivity, smoking and alcohol consumption, significantly increase the risk of NCDs in the working-age population in Mexico (0.23), while vigorous physical activity and salary has a negative impact (−0.17 and −0.23, respectively) on the presence of NCDs. This study highlights the ability of machine learning and SEM approaches to model the impact of health habits on NCDs for the economically active population in Mexico. ©The authors © Mathematics ©MDPI.
Some of the metrics are blocked by your
consent settings
Causal Artificial Intelligence in Legal Language Processing: A Systematic Review
(MDPI, 2025)
;
Recent advances in legal language processing have highlighted limitations in correlation-based artificial intelligence approaches, prompting exploration of Causal Artificial Intelligence (AI) techniques for improved legal reasoning. This systematic review examines the challenges, limitations, and potential impact of Causal AI in legal language processing compared to traditional correlation-based methods. Following the Joanna Briggs Institute methodology, we analyzed 47 papers from 2017 to 2024 across academic databases, private sector publications, and policy documents, evaluating their contributions through a rigorous scoring framework assessing Causal AI implementation, legal relevance, interpretation capabilities, and methodological quality. Our findings reveal that while Causal AI frameworks demonstrate superior capability in capturing legal reasoning compared to correlation-based methods, significant challenges remain in handling legal uncertainty, computational scalability, and potential algorithmic bias. The scarcity of comprehensive real-world implementations and overemphasis on transformer architectures without causal reasoning capabilities represent critical gaps in current research. Future development requires balanced integration of AI innovation with law’s narrative functions, particularly focusing on scalable architectures for maintaining causal coherence while preserving interpretability in legal analysis. ©The authors ©Entropy ©MDPI.
Some of the metrics are blocked by your
consent settings
Investment Portfolio Optimization Using Technical Indicators and White-Box Models
(IEEE, 2024-12-04)
Caro Reyna Luis Fernando
;
Arcos Bravo David Gamaliel
;
Quantitative 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.
Some of the metrics are blocked by your
consent settings
Knowledge and innovation management model in the mezcal industry in Mexico
(Elsevier, 2025)
;
Leyva-Hernández, Sandra Nelly
;
This research aims to study and analyze knowledge management in the mezcal sector in Mexico and its impact on the development of rural communities through Bayesian-networks with machine learning techniques. A model is made in which the critical factors that impact is identified and quantified to optimally manage the knowledge that generates value and translates into innovation and competitive advantages. The results show that the most relevant factors to adequate knowledge and innovation management are commercialization and marketing capacity, value system model, ancient knowledge, strategic business model, process management, competencies, Business structure model, Facilitators governments, universities, mezcaleros, and indigenous communities. ©The authors ©Elsevier Ltd.
11
Some of the metrics are blocked by your
consent settings
Evaluation of Dataset Distribution in Biomedical Image Classification Against Image Acquisition Distortions
(IEEE, 2024)
Aguilera-González, Santiago
;
Renza, Diego
;
One of the conditions expected when training a machine learning model is that the inference data should be independently and identically distributed (i.i.d.) with respect to the training data. However, as the real world evolves, this condition can be lost, which is known as shift distribution. This situation can affect the performance of a machine learning model, so the question is how to evaluate (without training a model) the presence of shift distribution. Consequently, this paper presents a proposal to determine the degree of distribution shift in medical image datasets in the face of possible distortions due to the capture system. The methodology is based on Cumulative Spectral Gradient (CSG) metric and it is applied to three biomedical imaging datasets extracted from MedMNIST, an initiative that has compiled several standardized biomedical datasets: PneumoniaMNIST, BreastMNIST and RetinaMNIST. Thanks to this methodology, it is possible to evaluate which types of modifications have a greater impact on the generalization of the models, as well as to determine if there are classes more affected by corruptions. ©The authors ©IEEE.
6
Some of the metrics are blocked by your
consent settings
Optimal Dataset Size for Fine-Tuning sEMG-Based Hand Gesture Recognition in Rehabilitation Prosthesis
(IEEE, 2024)
Escobedo-Gordillo, Andrés
;
;
;
;
Franco-Gaona, Erick
Surface electromyography (sEMG) has become a vital tool for controlling prostheses and rehabilitation using hand gesture recognition. However, the process of fine-tuning machine learning models to individual users often requires considerable amounts of data, which can be challenging to obtain due to user fatigue and discomfort. This work investigates the optimal dataset size needed for fine-tuning a pretrained Convolutional Neural Network (CNN) model for hand gesture recognition, using the NinaPro DB2 dataset. Our results show that training on just a third of the dataset achieves over 90% accuracy, highlighting a significant reduction in the data requirements compared to traditional methods. This approach can minimize the burden of data collection on users, making sEMG-based rehabilitation devices more practical and accessible. ©The authors ©IEEE
8

CRIS

Browse

Filters

Settings

Sort By

Results per page

Search Results