Prospective narratives on global issues: An AI-based pedagogical model for assessing complex thinking

This study proposes a pedagogical model for the creation and development of case studies, following a narrative scheme, that will allow the challenges and issues surrounding the megatrends of the 4th Industrial Revolution, specifically with the megatrend "People and the Internet". This proposal will be framed in an online learning environment, it has been designed as the second stage of the educational platform that will guide university students in the process of an ideathon to address the megatrends of the 4th Industrial Revolution. The aim of designing a model for creating case studies related to the megatrends is to foster complex thinking in university students, especially innovative thinking as one of the sub-competences of complex thinking. Fostering complex thinking highlights the importance of cognitive, practical and adaptive skills to address interdisciplinary challenges. The Design Process and Practice methodology and the case study methodology itself were used to develop the narrative case study design model. The outcome of this study adds a tangible dimension that enriches the debates on education and complex systems thinking in the context of the Fourth Industrial Revolution

Redes neuronales alfa-beta sin pesos: teoría y factibilidad de implementación

Tesis (Doctorado en Ciencias de la Computación), Instituto Politécnico Nacional, CIC, 2007, 1 archivo PDF, (104 páginas). tesis.ipn.m

Redes neuronales alfa-beta sin pesos: teoría y factibilidad de implementación

aportación de la tesis, se definen y ejemplifican las operaciones Alfa Generalizada, Sigma-Alfa y Sigma-Beta. Acto seguido, en términos de las operaciones Alfa, Beta, Alfa Generalizada, Sigma-Alfa y Sigma-Beta se crean y diseñan los algoritmos de aprendizaje y recuperación de patrones de CAINN, tomando como base los algoritmos de aprendizaje y recuperación de la red neuronal sin pesos denominada ADAM. Esta es la contribución central. Un aspecto que se ha considerado importante en este trabajo de tesis, por las ventajas de rapidez de operación, es la incorporación del hardware. En este sentido, se diseñan e implementan en FPGAs las tres operaciones: Alfa, Beta y Alfa Generalizada y, con base en ello, se diseña e implementa una arquitectura hardware para las fases de aprendizaje y recuperación de patrones de CAINN. Los aspectos formal y experimental están presentes de manera importante en elncontenido de esta tesis. Por el lado formal, con base en la teoría de los circuitos booleanos y de la clase de complejidad NC, se establecen formalmente y se sustentan teóricamente las condiciones necesarias y suficientes de equivalencia entre las redes neuronales sin pesos y los circuitos booleanos; se realiza, además, un estudio completo de factibilidad de implementación de CAINN. El aspecto experimental se cubre ampliamente dado que se diseñan y realizan experimentos de aplicación de CAINN en bases de datos conocidas, cuyos resultados se reportan en el capítulo de experimentos. Adicionalmente, se llevan a cabo y se reportan, estudios comparativos del rendimiento de CAINN respecto de ADAM y otros modelos, cuyos resultados exhiben la superioridad de CAINN respecto de ADAM, su contraparte como red neuronal sin pesos, y sobre otros modelos inmersos en el estado del arte de las redes neuronales o las memorias asociativas, sin olvidar la presencia de los efectos del Teorema No free lunch. Con el desarrollo de este trabajo de tesis se muestra claramente la posibilidad real de que en México se ideen, diseñen e implementen nuevas formas de atacar exitosamente problemas importantes en diversos ámbitos del quehacer humano. Se ilustra, de manera específica, uno de los resultados relevantes que ha obtenido el Grupo Alfa-Beta, a través del trabajo creativo de uno e sus miembros, el autor de esta tesis. Asimismo, se proporciona nuevo material original relacionado con los modelos asociativos Alfa-Beta a los grupos de investigación que desarrollan proyectos relacionados con las ciencias de la computación coadyuvando, con ello, al logro de la máxima del IPN: “La Técnica al Servicio de la Patria”

Ramath: aplicación móvil para la enseñanza de matemáticas

This paper presents the Ramath mobile application (app) and its second version using a head mounted display (HMD). Both use augmented reality with the support of game play mechanics in a non-game application, oriented to help 12 to 15 years old students (male and female) to learn several math subjects. We associate concepts such as «gamification» and «problem-based learning» (PBL) within the app, making the subjects much more interesting to the students and encouraging them to be fearless of math by taking advantage of the humans' psychological predisposition to engage in gaming. With the collected information from the research and the help of a predictive algorithm, we try to show how much the technology can help in education.Este trabajo presenta la aplicación móvil Ramath y su segunda versión utilizando un head mounted display (HMD). Ambas usan la realidad aumentada con el apoyo de mecánicas de juego en una aplicación no lúdica, orientada a ayudar a estudiantes (hombres y mujeres) de 12 a 15 años a aprender varias asignaturas de matemáticas. Asociamos conceptos como la «gamificación» y el «aprendizaje basado en problemas» (ABP) dentro de la aplicación, haciendo que los subproyectos sean mucho más interesantes para los estudiantes, animándolos a no tener miedo a las matemáticas, aprovechando la predisposición psicológica de los humanos a participar en juegos. Con la información recopilada en la investigación y la ayuda de un algoritmo predictivo, tratamos de mostrar hasta qué punto la tecnología puede ayudar en la educación

Ramath: mathematics teaching app

This paper presents the Ramath mobile application (app) and its second version using a head mounted display (HMD). Both use augmented reality with the support of game play mechanics in a non-game application, oriented to help 12 to 15 years old students (male and female) to learn several math subjects. We associate concepts such as «gamification» and «problem-based learning» (PBL) within the app, making the subjects much more interesting to the students and encouraging them to be fearless of math by taking advantage of the humans' psychological predisposition to engage in gaming. With the collected information from the research and the help of a predictive algorithm, we try to show how much the technology can help in education

A Novel Bioinspired Algorithm for Mixed and Incomplete Breast Cancer Data Classification

The pre-diagnosis of cancer has been approached from various perspectives, so it is imperative to continue improving classification algorithms to achieve early diagnosis of the disease and improve patient survival. In the medical field, there are data that, for various reasons, are lost. There are also datasets that mix numerical and categorical values. Very few algorithms classify datasets with such characteristics. Therefore, this study proposes the modification of an existing algorithm for the classification of cancer. The said algorithm showed excellent results compared with classical classification algorithms. The AISAC-MMD (Mixed and Missing Data) is based on the AISAC and was modified to work with datasets with missing and mixed values. It showed significantly better performance than bio-inspired or classical classification algorithms. Statistical analysis established that the AISAC-MMD significantly outperformed the Nearest Neighbor, C4.5, Naïve Bayes, ALVOT, Naïve Associative Classifier, AIRS1, Immunos1, and CLONALG algorithms in conducting breast cancer classification

Hand Movement Classification Using Burg Reflection Coefficients

Classification of electromyographic signals has a wide range of applications, from clinical diagnosis of different muscular diseases to biomedical engineering, where their use as input for the control of prosthetic devices has become a hot topic of research. The challenge of classifying these signals relies on the accuracy of the proposed algorithm and the possibility of its implementation in hardware. This paper considers the problem of electromyography signal classification, solved with the proposed signal processing and feature extraction stages, with the focus lying on the signal model and time domain characteristics for better classification accuracy. The proposal considers a simple preprocessing technique that produces signals suitable for feature extraction and the Burg reflection coefficients to form learning and classification patterns. These coefficients yield a competitive classification rate compared to the time domain features used. Sometimes, the feature extraction from electromyographic signals has shown that the procedure can omit less useful traits for machine learning models. Using feature selection algorithms provides a higher classification performance with as few traits as possible. The algorithms achieved a high classification rate up to 100% with low pattern dimensionality, with other kinds of uncorrelated attributes for hand movement identification

Development of an Intelligent System for the Monitoring and Diagnosis of the Well-Being

Today, society is more aware of their well-being and health, making wearable devices a new and affordable way to track them continuously. Smartwatches allow access to daily vital physiological measurements, which help people to be aware of their health status. Even though these technologies allow the following of different health conditions, their application in health is still limited to the following physical parameters to allow physicians treatment and diagnosis. This paper presents LM Research, a smart monitoring system mainly composed of a web page, REST APIs, machine learning algorithms, psychological questionnaire, and smartwatches. The system introduces the continuous monitoring of the users’ physical and mental indicators to prevent a wellness crisis; the mental indicators and the system’s continuous feedback to the user could be, in the future, a tool for medical specialists treating well-being. For this purpose, it collects psychological parameters on smartwatches and mental health data using a psychological questionnaire to develop a supervised machine learning wellness model that predicts the wellness of smartwatch users. The full construction of the database and the technology employed for its development is presented. Moreover, six machine learning algorithms (Decision Tree, Random Forest, Naive Bayes, Neural Networks, Support Vector Machine, and K-nearest neighbor) were applied to the database to test which classifies better the information obtained by the proposed system. In order to integrate this algorithm into LM Research, Random Forest being the one with the higher accuracy of 88%