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Investment Portfolio Optimization Using Technical Indicators and White-Box Models
(IEEE, 2024-12-04)
Caro Reyna Luis Fernando
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Arcos Bravo David Gamaliel
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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.
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Knowledge and innovation management model in the mezcal industry in Mexico
(Elsevier, 2025)
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Leyva-Hernández, Sandra Nelly
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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.
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Evaluation of Dataset Distribution in Biomedical Image Classification Against Image Acquisition Distortions
(IEEE, 2024)
Aguilera-González, Santiago
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Renza, Diego
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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.
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Optimal Dataset Size for Fine-Tuning sEMG-Based Hand Gesture Recognition in Rehabilitation Prosthesis
(IEEE, 2024)
Escobedo-Gordillo, Andrés
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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
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Determining the medical Spanish translation capabilities of three artificial intelligence translation models for Mohs micrographic surgical instructions
(Elsevier Inc., 2024)
Scheinkman, Ryan
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Montoya, Sofia
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Náder, Maria
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Ramírez, Mariana
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Barbato, Kristiana
To the Editor: Artificial intelligence (AI) has been used to simplify medical-legal documentation.1 In order to protect patients from mistranslations, it is critical to assess the accuracy of AI translations. We attempted to assess the current translational capacities of 3 AI models for Mohs micrographic surgery documentation. The purpose of this analysis was to see if these programs had capabilities that were comparable to human medical translators and determine their capacity for future medical translation applications. In order to determine the validity of these models, preoperative and postoperative instructions from multiple sources were translated by Google Translate, Amazon Translate, and DeepL to Spanish from 3 publicly available academic center websites, specifically: the University of Mississippi Medical Center (University of Mississippi), University of Rochester, and Brigham Cancer Center.2-5 Accuracy of translation was then assessed by 3 native Spanish-speaking medical professionals and students that received C-1 levels on the Test of English as a Foreign Language demonstrating advanced English proficiency. ©The authors © Journal of the American Academy of Dermatology ©Elsevier Inc.
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Using Social Robotics to Identify Educational Behavior: A Survey
(MDPI, 2024)
Romero-C. de Vaca, Antonio J.
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Melendez-Armenta, Roberto Angel
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The advancement of social robots in recent years has opened a promising avenue for providing users with more accessible and personalized attention. These robots have been integrated into various aspects of human life, particularly in activities geared toward students, such as entertainment, education, and companionship, with the assistance of artificial intelligence (AI). AI plays a crucial role in enhancing these experiences by enabling social and educational robots to interact and adapt intelligently to their environment. In social robotics, AI is used to develop systems capable of understanding human emotions and responding to them, thereby facilitating interaction and collaboration between humans and robots in social settings. This article aims to present a survey of the use of robots in education, highlighting the degree of integration of social robots in this field worldwide. It also explores the robotic technologies applied according to the students’ educational level. This study provides an overview of the technical literature in social robotics and behavior recognition systems applied to education at various educational levels, especially in recent years. Additionally, it reviews the range of social robots in the market involved in these activities. The objects of study, techniques, and tools used, as well as the resources and results, are described to offer a view of the current state of the reviewed areas and to contribute to future research. ©The authors ©MDPI.
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An intelligent climate monitoring system for hygrothermal virtual measurement in closed buildings using Internet-of-things and artificial hydrocarbon networks
(Elsevier, 2024)
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Botero-Valencia, Juan
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Botero Valencia, Juan
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Marquez-Viloria, David
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Castano-Londono, Luis
Studies analyzing indoor thermal environments comprising temperature and humidity may be insufficient when obtaining data from sensors, which may be susceptible to inaccurate or failed information from internal and external factors. Therefore, this study proposes an intelligent climate monitoring using a supervised learning method for virtual hygrothermal measurement in enclosed buildings used to predict temperature and relative humidity when a sensor failure is detected. The methodology comprises the data collection from a wireless sensor network, the building of the learning model for predicting the dynamics of environmental variables, and the implementation of a sensor failure detection model. We use an artificial hydrocarbon network as the learning model for their simplicity and effectiveness under uncertain and noisy data. The experiments use data acquired in two settings: (1) a laboratory office and (2) a museum storage room. The first scenario has multiple workstations, and the staff turns on or off the air conditioning depending on the feeling of comfort, generating an uncontrolled environment for the variables of interest. The second scenario has controlled temperature and humidity to ensure the conservation conditions of the museum pieces. Both scenarios used 12 sensors that acquired data for one month, providing an average of 58,300 values for each variable. Results of the proposed methodology provide 95% of accuracy in terms of sensor failure detection and identification, and less than 0.22% of tolerance variability in temperature and humidity after sensor accommodation in both scenarios. ©Elsevier
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Preface : Advances in Soft Computing : 22nd Mexican International Conference on Artificial Intelligence, MICAI 2023, Yucatán, Mexico, November 13–18, 2023, Proceedings, Part II
(Springer, 2024-01-01)
Calvo, Hiram
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The Mexican International Conference on Artificial Intelligence (MICAI) is a yearly international conference series that has been organized by the Mexican Society for Artificial Intelligence (SMIA) since 2000. MICAI is a major international artificial intelligence (AI) forum and the main event in the academic life of the country’s growing AI community. This year, MICAI 2023 was graciously hosted by the Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS) and the Universidad Autónoma del Estado de Yucatán (UAEY). The conference presented a cornucopia of scientific endeavors. ©Springer.
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Eye Control and Motion with Deep Reinforcement Learning: In Virtual and Physical Environments
(Springer, 2024-01-01)
Arizmendi, Sergio
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Paz, Asdrubal
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González, Javier
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Attention mechanism in computer vision refers to scan, detect, and track a target object. This paper aims to develop and virtually train a machine learning model for object attention mechanism, combining object detection and mechanical automation. For this, we use Unity 3D Engine to model a simple scene in which two virtual cameras align together to realize a monocular attention in specific objects. Deep reinforcement learning, via ML-agent’s library, was used to train a model that aligns the virtual cameras. Moreover, the model was transferred to a physical camera to replicate the performance of attention mechanism.
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Catastrophic Health Spending by COVID-19 in the Mexican Insurance Sector
(Springer, 2024-01-01)
Domínguez-Gutiérrez, Ulises
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The COVID-19 pandemic that the world has been suffering for 3 years has generated major impacts worldwide, both in public health systems and in the private insurance industry. The high costs of care derived from cases with complications have likewise generated a great impact on the private insurance industry. In the case of Mexico, the mortality rates observed are among the first places, in addition to generating a great impact on private insurance. This work deals with the measurement of the impact of catastrophic expenses derived from COVID-19 in an insurance company; using a set of machine learning models, the key variables in the estimation of patients with potential catastrophic expenses were determined. The results show that the estimated classification model has a positive performance in addition to allowing the identification of the main risk factors of the insured as well as their potentially catastrophic impact on insurance companies.© 2024 Springer Nature
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