Now showing 1 - 10 of 147
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Explainable artificial hydrocarbon networks classifier applied to preeclampsia

2024 , Ponce, Hiram , Martinez-Villaseñor, Lourdes , Martínez Velasco, Antonieta Teodora

Explainability is crucial in domains where system decisions have significant implications for human trust in black-box models. Lack of understanding regarding how these decisions are made hinders the adoption of so-called clinical decision support systems. While neural networks and deep learning methods exhibit impressive performance, they remain less explainable than white-box approaches. Artificial Hydrocarbon Networks (AHN) is an effective black-box model that can be used to support critical clinical decisions if accompanied by explainability mechanisms to instill confidence among clinicians. In this paper, we present a use case involving global and local explanations for AHN models, provided with an automatic procedure so-called eXplainable Artificial Hydrocarbon Networks (XAHN). We apply XAHN to preeclampsia prognosis, enabling interpretability within an accurate black-box model. Our approach involves training a suitable AHN model using the cross-validation with ten repetitions, followed by a comparative analysis against four well-known machine learning techniques. Notably, the AHN model outperformed the others, achieving an F1-score of 74.91%. Additionally, we assess the efficacy of our XAHN explainer through a survey applied to clinicians, evaluating the goodness and satisfaction of the provided explanations. To the best of our knowledge, this work represents one of the earliest attempts to address the explainability challenge in preeclampsia prediction.© 2024 The Author(s). Published by Elsevier Inc.

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Contactless Video-Based Vital-Sign Measurement Methods: A Data-Driven Review

2024-01-01 , Brieva, Jorge , Moya-Albor, Ernesto , Ponce, Hiram , Escobedo-Gordillo, Andrés

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Machine Learning Techniques in Credit Default Prediction

2022 , Malagon, Emmanuel , Troncoso, Daniel , Rubio, Andrés , Ponce, Hiram

Digital transformation after the pandemic is a must if a company wants to survive in a highly competitive environment. Machine Learning (ML) applications are no strangers to Digital Transformations, and banks are looking for ways to improve efficiency by means of similar technologies. In this work, we propose a machine learning model for predicting the credit default using the LendingClub public dataset. The accepted loans include data ranging from 2007 to 2017. For this purpose, we implement support vector machines and logistic regression models. The results showed that support vector machines is a high accurate model (93%) for predicting the credit default. © 2023 Springer Nature Switzerland AG. Part of Springer Nature.

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Sentiment Analysis on Twitter About COVID-19 Vaccination in Mexico

2021 , Bernal, Claudia , Bernal, Miguel , Noguera, Andrei , Ponce, Hiram , Avalos Gauna, Edgar

This paper conducts a sentiment analysis of Twitter’s posts, between late October 2020 and late April 2021, regarding COVID-19 vaccination campaign in Mexico through several machine learning models such as Logistic Regression, Neuronal Network, Naive Bayes and Support Vector Machine. To prepare data, Natural Language Processing techniques were used such as tokenization, stemming, n-grams and stopwords. The best performance was achieved by Logistic Regression with an accuracy score of 83.42% while classifying tweets according to a positive or negative sense. This work suggests that sentiment analysis with Twitter information allows to witness a relevant part of the public discussion around specific topics. For this study, the tweets analyzed showed a similar behavior to other search and reference electronic tools, such as Google Trends regarding conversation around COVID. In addition, the present analysis allows the classification and tendency of public opinion. Furthermore, this study shows that measuring people’s opinion through machine learning and natural language processing techniques can generate significant benefits for institutions and businesses given that obtaining information on Twitter is less expensive and can be processed and analyzed faster than other opinion analysis techniques such as surveys or focus groups. © 2021, Springer Nature Switzerland AG.

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Ethical Design Framework for Artificial Intelligence Healthcare Technologies

2024-01-01 , Martinez-Villaseñor, Lourdes , Ponce, Hiram

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A Methodology Based on Deep Q-Learning/Genetic Algorithms for Optimizing COVID-19 Pandemic Government Actions

2020 , Miralles-Pechuán, Luis , Jiménez, Fernando , Ponce, Hiram , Martinez-Villaseñor, Lourdes

Whenever countries are threatened by a pandemic, as is the case with the COVID-19 virus, governments need help to take the right actions to safeguard public health as well as to mitigate the negative effects on the economy. A restrictive approach can seriously damage the economy. Conversely, a relaxed one may put at risk a high percentage of the population. Other investigations in this area are focused on modelling the spread of the virus or estimating the impact of the different measures on its propagation. However, in this paper, we propose a new methodology for helping governments in planning the phases to combat the pandemic based on their priorities. To this end, we implement the SEIR epidemiological model to represent the evolution of the COVID-19 virus on the population. To optimize the best sequences of actions governments can take, we propose a methodology with two approaches, one based on Deep Q-Learning and another one based on Genetic Algorithms. The sequences of actions (confinement, self-isolation, two-meter distance or not taking restrictions) are evaluated according to a reward system focused on meeting two objectives: firstly, getting few people infected so that hospitals are not overwhelmed, and secondly, avoiding taking drastic measures which could cause serious damage to the economy. The conducted experiments evaluate our methodology based on the accumulated rewards during the established period. The experiments also prove that it is a valid tool for governments to reduce the negative effects of a pandemic by optimizing the planning of the phases. According to our results, the approach based on Deep Q-Learning outperforms the one based on Genetic Algorithms. © 2020 ACM.

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A Machine Learning Approach for Modeling Safety Stock Optimization Equation in the Cosmetics and Beauty Industry

2021 , Díaz, David , Marta, Regina , Ortega, Germán , Ponce, Hiram

Safety Stock is generally accepted as an appropriate inventory management strategy to deal with the uncertainty of demand and supply, as well as for limiting the risk of service loss and overproduction [6]. In particular, companies from the cosmetics and beauty industry face additional inventory management challenges derived from the strict regulatory standards applicable in different jurisdictions, in addition to the constantly changing trends, which highlight the importance of defining an accurate safety stock. In this paper, on the basis of the Linear Regression, Decision Trees, Support Vector Machine (“SVM”) and Neural Network machine learning techniques, we modeled a general Safety Stock equation and one per product category for a multinational enterprise operating in the cosmetics and beauty industry. The results of our analysis indicate that the Linear Regression is the most accurate model to generate a reasonable and effective prediction of the company’s Safety Stock. © 2021, Springer Nature Switzerland AG.

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From Project-Based Learning to Innovative Technologies in Mechatronics Course: A Case Study in a Private University in Mexico City

2024-01-01 , Ponce, Hiram , Moya-Albor, Ernesto , Brieva, Jorge

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Non-contact breathing rate monitoring system using a magnification technique and convolutional networks

2020 , Brieva, Jorge , Ponce, Hiram , Moya-Albor, Ernesto

In this paper, we present a new non-contact strategy to estimate the breathing rate based on the Eulerian motion video magnification technique and a system based on a Convolutional Neural Network (CNN). After the magnification procedure, a CNN is trained to detect the inhalation and exhalation frames in the video. From this classification, the respiratory rate is estimated. The magnification procedure was carried out using the Hermite decomposition. Two strategies are used as input to the CNN. A CNN-ROI proposal where a region of interest is selected manually on the image frame and in the second case, a CNN-Whole-Image proposal where the entire image frame is selected. Finally, the RR is estimated from the classified frames. The CNN-ROI proposal is tested on five subjects in lying face down position and it is compared to a procedure using different image processing steps to tag the frames as inhalation or exhalation. The mean average error in percentage obtained for this proposal is 2.326±1.144%. The CNN-whole-image proposal is tested on eight subjects in lying face down position. The mean average error in percentage obtained for this proposal is 2.115 ± 1.135%. © COPYRIGHT 2020 SPIE. Downloading of the abstract is permitted for personal use ONLY.

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Application of Convolutional Neural Networks for Fall Detection Using Multiple Cameras

2020 , Espinosa Loera, Ricardo Abel , Ponce, Hiram , Gutiérrez, Sebastián , Martinez-Villaseñor, Lourdes , Moya-Albor, Ernesto , Brieva, Jorge

Currently one of the most important research issue for artificial intelligence and computer vision tasks is the recognition of human falls. Due to the current exponential increase in the use of cameras is it common to use vision-based approach for fall detection and classification systems. On another hand deep learning algorithms have transformed the way that we see vision-based problems. The Convolutional Neural Network (CNN) as deep learning technique offers more reliable and robust solutions on detection and classification problems. Focusing only on a vision-based approach, for this work we used images from a new public multimodal data set for fall detection (UP-Fall Detection dataset) published by our research team. In this chapter we present fall detection system using a 2D CNN analyzing multiple camera information. This method analyzes images in fixed time window frames extracting features using an optical flow method that obtains information of relative motion between two consecutive images. For experimental results, we tested this approach in UP-Fall Detection dataset. Results showed that our proposed multi-vision-based approach detects human falls achieving 95.64% in accuracy with a simple CNN network architecture compared with other state-of-the-art methods.