Computer and Electronic Sciences: Theory and Applications

Machine Learning in Cardiovascular Disease Detection: An Experimental Analysis of Techniques

2025-06-10T00:48:18+00:00

Introduction: Cardiovascular diseases (CVD) are the leading cause of mortality worldwide. Early detection is essential for implementing preventive strategies to mitigate serious complications and reduce the mortality rate. In this context, machine learning techniques have become a key tool for developing effective predictive models in the health field.
Objective: To improve accuracy in identifying patients at risk of CVD by implementing the wrapper method for feature selection in combination with unsupervised learning algorithms.
Methods: Based on the “Cleveland Heart Disease Data Set” dataset from the Machine Learning repository of the ICU KDD. Information Gain and Chi-Square feature selection techniques were applied to identify the most relevant variables in the classification process. Subsequently, several models were trained, including C4.5, Random Forest, SOM, and GHSOM neural networks, and Naive Bayes Tree, to automatically classify the probability of presenting a cardiovascular risk condition.
Results: The experimental results show that the Random Forest model, combined with 10-fold cross-validation and the Information Gain technique, achieved the best performance, with a precision of 85.70% and an accuracy of 87.10%.
Conclusions: The results of the simulations indicate that the combination of the Information Gain feature selection method with the Random Forest classifier offers the best performance in the identification of cardiovascular diseases, reaching an accuracy that is accepted as optimal compared to the reviewed literature.

Detection of Depression Symptoms on Social Media Using Machine Learning and Facial Analysis: An Integrated Approach

2026-06-16T23:24:44+00:00

Introduction: The early detection of depressive symptoms is a critical challenge in mental health, particularly due to the limitations of traditional diagnostic tools and the growing relevance of digital platforms as sources of behavioral and emotional information.
Objective: To develop an automated system for detecting depressive indicators through the analysis of Instagram content, integrating web scraping techniques, natural language processing, facial emotion recognition, and machine learning algorithms.
Method: A computational approach was employed, combining textual classification and facial emotion analysis. For the textual component, Naive Bayes, Logistic Regression, and Random Forest algorithms were evaluated using a dataset of approximately 200,000 text records from Reddit, which was used exclusively for training and evaluating the text classifier. Facial emotion analysis was performed independently on images extracted from the analyzed Instagram profiles. The system was implemented through a Streamlit interface.
Results: The results showed that the Naive Bayes model achieved the best performance for textual classification, with an accuracy of 90% and high recall in detecting depressive indicators. The integration of text analysis and facial emotion recognition allowed the system to compensate for the limitations of each method when used separately and to generate both individual and global reports.
Conclusions: The proposed system contributes to the development of non-invasive tools for the early detection of depressive symptoms, supporting timely interventions and demonstrating the potential of combining machine learning, natural language processing, and facial emotion recognition in digital mental health contexts.

An Empirical Analysis of RSA Common-Factor Vulnerabilities in Contemporary TLS Certificates from Latin America

2026-06-16T23:24:46+00:00

Introduction: The security of RSA cryptographic systems critically depends on the proper generation of large prime numbers. Entropy failures during this process may cause different keys to share common prime factors, compromising private keys and exposing digital systems to cryptographic attacks.
Objective: To analyze whether the vulnerability associated with shared prime factors in RSA keys, reported in previous studies, persists in a contemporary context within TLS certificates linked to Latin American domains.
Method: A quantitative and computational approach was adopted, based on the cryptographic analysis of RSA public keys. A dataset of 1,444 valid RSA moduli was collected from TLS certificates associated with Latin American domains, using Certificate Transparency logs obtained through the crt.sh platform. Subsequently, an exhaustive pairwise greatest common divisor, GCD, computation was performed to identify possible shared prime factors among the analyzed moduli.
Results: The results showed no evidence of RSA moduli sharing non-trivial common factors within the sample analyzed. This indicates that the specific vulnerability reported in earlier studies, related to entropy failures and accidental reuse of prime factors, was not observed in the studied certificate set.
Conclusions: The findings suggest significant improvements in cryptographic key generation practices over the past decade, at least within the sample analyzed. However, the study highlights the importance of continuous cryptographic auditing to promptly detect potential weaknesses in digital security infrastructures.

Artificial Intelligence and Decision Systems in Emerging Economies: A Bibliometric and Thematic Evolution Analysis

2026-06-16T23:24:43+00:00

Introduction: The application of artificial intelligence to decision-making has become a relevant field of study for management and innovation, as it allows researchers to examine not only what is being studied, but also how this knowledge connects with organizational realities.
Objective: To analyze the evolution of the field of artificial intelligence applied to decision-making, with particular attention to the differences between global scientific production and research developed in more applied contexts.
Method: A quantitative bibliometric approach was employed, based on the comparative analysis of two corpora: one with international scope and another corresponding to an emerging journal. Frequency analysis, proportions, Pearson correlation, and linear regression modeling were used to identify patterns of convergence and divergence in the thematic structure of the field.
Results: The results reveal an inverse relationship between both corpora, with a strong concentration on artificial intelligence and decision systems at the global level, contrasted with a more balanced and contextual distribution in the emerging corpus. This difference suggests that the development of the field is not uniform, but rather reflects different levels of maturity and contextual conditions.
Conclusions: Emerging economies, far from being lagging behind, represent a strategic space for the application and adaptation of AI-based decision systems, opening new opportunities for research closely linked to real organizational challenges.

Analysis of Sales, Profitability, and Customer Behavior in the Superstore Dataset

2026-06-16T23:24:45+00:00

Introduction: The analysis of sales, profitability, and customer behavior has become essential for improving decision-making in the retail sector, especially through the use of business intelligence tools that allow data to be explored visually and analytically.
Objective: To analyze the factors that influence business performance in the retail sector using the Sample Superstore dataset, with emphasis on the effect of discounts on profitability, customer segment behavior, and geographic differences in sales and profit margins.
Method: A descriptive and analytical approach was applied using Microsoft Power BI as the main tool for data visualization and exploration. The analysis was structured around four research questions related to product category performance, discount impact, customer segment profitability, and regional opportunities for improvement.
Results: The results show that the Technology category generates the highest sales and profit levels, while some subcategories, such as Tables, present significant losses despite having considerable sales volume. A negative relationship was identified between discounts and profitability, especially when discounts exceed moderate levels. The Consumer segment generates the highest sales volume, whereas the Corporate segment shows more stable and profitable behavior. In addition, states such as Texas and Illinois were identified as areas with high sales volume but low profit margins.
Conclusions: The findings suggest the need to optimize pricing strategies, control discount policies, strengthen high-value customer segments, and improve regional commercial management in order to increase profitability and support more effective decision-making in the retail sector.