CRIS

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

Browse

Search Results

Now showing 1 - 6 of 6

Some of the metrics are blocked by your
consent settings
MICAI and the Making of AI in Mexico Through 25 Years of Data-Driven Insight
(Springer Nature Switzerland, 2025)
Avalos-Gauna, Edgar
;
Palafox Novack, Leon Felipe
;
Martinez-Villaseñor, Lourdes
This study investigates the integration of facial emotion recognition (FER) into Intelligent Tutoring Systems (ITS), with the aim of identifying the emotions that emerge throughout the learning process. Building on prior research, we argue that understanding students’ emotional states enables the delivery of more adaptive and effective feedback, thereby improving learning outcomes. A Deep Convolutional Neural Network (DCNN) was trained on the FER2013+ dataset, achieving a top-3 accuracy of 95.24% in classifying facial expressions across eight emotion categories. The model was integrated with MediaPipe to enable real-time emotion detection from video streams using a standard laptop camera, facilitating practical deployment in educational settings. Thirteen high school and early university students interacted with OATutor—an open-source ITS—while their facial expressions and on-screen activities were recorded. Emotional data from each frame was synchronized with an academic event log documenting actions such as starting a lesson, requesting help, or submitting answers. Results show that “surprise” was the most frequently observed emotion (over 85% of instances), whereas “anger,” “sadness,” and “contempt” appeared only in specific learning scenarios, particularly when students faced cognitive challenges or achieved multiple correct responses. Despite the absence of affective feedback from the system, students’ emotions fluctuated dynamically, suggesting active self-regulation processes. These findings demonstrate the feasibility of FER-enhanced ITS in real-world educational environments and underscore the need for future work integrating multimodal data and personalization strategies to optimize affective responsiveness in intelligent learning contexts. ©The authors ©Springer.
Some of the metrics are blocked by your
consent settings
Sphonic: Development of a Mobile Application Using AI and AR for Learning Biomedical Concepts
(Springer Nature Switzerland, 2025)
Escobar-Castillejos, Daisy
;
Báez Gómez Tagle, Enrique Ulises
;
Chavarría-Reyes, Fernando Mauricio
;
Sigüenza Noriega, Iñaki
;
Cruz-Ledesma, Iván
This chapter covers the development of Sphonic, an iOS mobile application that combines artificial intelligence (AI) and augmented reality (AR) to improve biomedical education. Sphonic was designed to employ SwiftUI for its front end, while its back end is based on Django and hosted on Amazon Web Services. Sphonic intends to assist in explaining complex topics in chemistry and biology. Using Apple’s ARKit framework, Sphonic allows students to explore dynamic representations of DNA architecture, chemical bonding interactions, and biological processes to promote engagement and retention. On the other hand, the application features an OCR-based chemical equation solver, using Amazon Bedrock’s generative AI capabilities, that balances equations and offers comprehensive, step-by-step guidance, addressing conceptual deficiencies for learners. Additionally, Sphonic implements secure authentication protocols to protect user data and features a user-friendly interface that simplifies navigation. This chapter concludes by emphasizing the importance of AI and AR in modern education, demonstrating how these technologies could democratize access and understanding of scientific information and foster creativity in academic environments. ©The authors ©Srpinger.
Some of the metrics are blocked by your
consent settings
A Novel Ethical Design Framework Applied to Image Classification Challenges in the Fashion Industry
(Springer Nature Switzerland, 2025)
Guillen Alvarez, Luis
;
Martinez-Villaseñor, Lourdes
;
Ponce, Hiram
As artificial intelligence (AI) continues to play a pivotal role in image classification applications, the ethical implications of these technologies become increasingly significant. This paper explores the intersection of AI and ethics in the context of image classification, specifically focusing on the application of ethical design principles through a framework for a use of case in the fashion industry involving bags images and social media. This work delves into the integration of a comprehensive ethical framework around all the design process. The case study involves the development and implementation of a neural network tailored for bag image classification, leveraging transfer learning techniques. Through a meticulous examination of the ethical dimensions inherent in image classification, the study aims to establish a foundation for responsible and transparent AI practices. ©The authors ©Springer.
Some of the metrics are blocked by your
consent settings
Acerca de las etapas del constitucionalismo y de las generaciones de derechos humanos y de las leyes de protección de datos
(Universidad Panamericana, 2024-07-13)
Puccinelli, Oscar
Las revoluciones independentistas e industriales, las guerras mundiales, la carrera espacial y las dos guerras frías, entre otros fenómenos, han tenido un alto impacto en distintos ámbitos y de hecho constituyeron hitos que sirvieron de bisagras para definir etapas en el constitucionalismo y en los derechos humanos. En la evolución de la protección de éstos, nos centramos en la gestación y desarrollo de uno que está estrechamente vinculado a las tecnologías de la información y de la comunicación (TICs) y que surgió como respuesta ante los avances sobre los derechos de las personas –en especial pero no exclusivamente sobre la intimidad- por parte de quienes tratan información personal: el “derecho a la protección de datos” (inicialmente rotulado “derecho a la autodeterminación informativa, que actualmente aporta una cantidad ingente de contenidos a los “derechos digitales”), refiriendo a las principales normativas gestadas en sus cinco generaciones, desplegadas entre 1970 y la actualidad.
Some of the metrics are blocked by your
consent settings
Leveraging mHealth and Artificial Intelligence for Enhanced Health Indicators, A TwiMV Framework Proposal
(Springer Nature Switzerland, 2025)
Domínguez-Miranda, Sergio Arturo
;
Rodríguez Aguilar, Román
Technological advances and artificial intelligence (AI) are transforming healthcare by improving prevention, diagnosis, and remote health monitoring. This paper explores the current landscape of the potential application of digital technologies to the healthcare sector. The design of the TwiMV system is proposed to integrate diverse health data for patient diagnosis and monitoring, in a comprehensive framework, encompassing biochemical studies, genetic analysis, medical images, biometric data, dietary and lifestyle information, and wearable data. The proposal involves real-time processing through cloud-based platforms, as well as the integration of artificial intelligence algorithms and digital technologies. The proposed system aims to improve health management through personalized interventions, with specialized modules addressing priority conditions such as stroke, cardiovascular disease, oncology, diabetes, and neurological disorders. ©The authors ©Springer Nature.
Some of the metrics are blocked by your
consent settings
Artificial Intelligence–Enabled Analysis of Thermography to Diagnose Acute Decompensated Heart Failure
(Elsevier BV, 2025-07)
Atamañuk, Andrés Nicolás
;
Gandino, Ignacio Javier
;
Miranda, María Noralí
;
Cardozo, Leandro Martín
;
Escalante, Sergio Exequiel
Background: Analyzing skin temperature in heart failure is an important medical practice that could assist to identify poor perfusion. Thermography, a technique that captures infrared radiation from tissues, could quantify these temperatures and thermal gradients. It has not been evaluated in patients with acute decompensated heart failure (ADHF) before. Objectives: The purpose of this study was to assess the performance of thermography in the diagnosis of ADHF. Methods: A cross-sectional study was performed, including consecutive patients hospitalized with ADHF diagnosed by an expert heart failure team. Patients hospitalized for other cardiac disorders without ADHF were included as controls. Ten thermal photos of each patient were taken within the first 4 hours after admission in a cardiac care unit. Specific thermal spots, averages, and gradients were analyzed. Thermography's diagnostic properties for ADHF detection were evaluated using machine learning with the extreme gradient boosting model. Results: Sixty patients were included: 30 cases with ADHF and 30 controls. The mean age was 63.4 years (SD: 13.3), and 38 (63.3%) were males. Thermal points and averages showed lower temperature, while gradients were higher in the ADHF group, being all statistically significant between groups. The properties of the blend between thermography and artificial intelligence to detect ADHF had 84% sensitivity and 52% specificity. The area under the curve was 0.82 (95% CI: 0.73-0.91). Conclusions: Thermography demonstrated differences between patients with ADHF and those with other cardiological disorders. In this proof of concept, combining thermography with artificial intelligence enabled the detection of ADHF in subjects hospitalized in a cardiac care unit. ©The authors ©JACC: Advances ©Elsevier.

CRIS

Browse

Filters

Settings

Sort By

Results per page

Search Results