Martinez-Villaseñor, Lourdes
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Preferred name
Martinez-Villaseñor, Lourdes
Official Name
Martínez Villaseñor, María de Lourdes
Alternative Name
lmartine
Main Affiliation
ORCID
Scopus Author ID
55521085100
Researcher ID
N-7607-2018

Research Output
Now showing 1 - 10 of 15
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Publication

Artificial Hydrocarbon Networks for Online Sales Prediction

2015 , Ponce, Hiram , Miralles-Pechuán, Luis , Martinez-Villaseñor, Lourdes

Online retail sales have been growing worldwide in the last decade. In order to cope with this high dynamicity and market share competition, online retail sales prediction and online advertising have become very important to answer questions of pricing decisions, advertising responsiveness, and product demand. To make adequate investment in products and channels it is necessary to have a model that relates certain features of the product with the number of sales that will occur in the future. In this paper we describe a comparative analysis of machine learning techniques against a novel supervised learning technique called artificial hydrocarbon networks (AHN). This method is a new type of machine learning that have proved to adapt very well to a wide spectrum of problems of regression and classification. Thus, we use artificial hydrocarbon networks for predicting the number of online sales, and then we compare their performance with other ten well-known methods of machine learning regression, obtaining promising results.

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Machine Learning Approach for Pre-Eclampsia Risk Factors Association

2018 , Martínez Velasco, Antonieta Teodora , Martinez-Villaseñor, Lourdes , Miralles-Pechuán, Luis

The preeclampsia/eclampsia syndrome is a multisystem disorder that usually includes cardiovascular changes, hematologic abnormalities, hepatic and renal impairment, and neurologic or cerebral manifestations. Preeclampsia (PE) is a clinical syndrome that afflicts 3–5% of pregnancies and it is a leading cause of maternal mortality, especially in developing countries. To understand in greater depth the preeclampsia/eclampsia syndrome, we applied some well-known Machine Learning (ML) techniques. ML has been successfully applied to medical research to improve the diagnosis and the prevention of complex diseases and syndromes. In our contribution, we have created a supervised model to predict if a patient suffers the disease. This model has been optimized by selecting the best features and by optimizing the threshold when predicting a class. We used these techniques to point out the most related features of the patients to the disease. Finally, we used interpretability techniques to extract and visualize through a decision tree the most relevant associations of the disease with the patients' features. © 2018 Association for Computing Machinery.

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Machine learning method to establish the connection between age related macular degeneration and some genetic variations

2016 , Martínez Velasco, Antonieta Teodora , Zenteno, Juan Carlos , Martinez-Villaseñor, Lourdes , Miralles-Pechuán, Luis , Pérez Ortiz, Andric Christopher , Estrada Mena, Francisco Javier

Medicine research based in machine learning methods allows the improvement of diagnosis in complex diseases. Age related Macular Degeneration (AMD) is one of them. AMD is the leading cause of blindness in the world. It causes the 8.7% of blind people. A set of case and controls study could be developed by machine-learning methods to find the relation between Single Nucleotide Polymorphisms (SNPs) SNP_A, SNP_B, SNP_C and AMD. In this paper we present a machine-learning based analysis to determine the relation of three single nucleotide SNPs and the AMD disease. The SNPs SNP_B, SNP_C remained in the top four relevant features with ophthalmologic surgeries and bilateral cataract. We aim also to determine the best set of features for the classification process. © Springer International Publishing AG 2016.

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A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks

2016 , Ponce, Hiram , Martinez-Villaseñor, Lourdes , Miralles-Pechuán, Luis

Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods.

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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 2020 perspective on “A novel methodology for optimizing display advertising campaigns using genetic algorithms”

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

Online advertising has become the most important area of publicity. From a post-2020 perspective, we identify three trends in online advertising comprising: the rapid evolution of online advertising mainly over mobile networks, how to cope with big companies leading digital marketing, and the exploration of new methods to handle the dynamics of the e-commerce ecosystem. We proposed a new methodology for online advertising in small ad networks using supervised machine learning and metaheuristic methods. Our research will be beneficial for addressing the above-mentioned trends in online advertising focusing on small ad networks. It contributes to the establishment of an information system technology and practice within the scope of the development of marketing business strategies in e-commerce. Currently, we are exploring how to improve the flexibility of our approach to make it easier to adapt to new ad campaigns, analyzing and comparing different computational methods, and how to increase the performance of presenting custom ads to users when dealing with small data sets. Online advertising in small ad networks will be very useful in the following years. Hence, there are still many challenges to be dealt with in order to implement it in the business strategies of the new digital marketing. © 2020 Elsevier B.V.

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Interoperability in Electronic Health Records Through the Mediation of Ubiquitous User Model

2016 , Martinez-Villaseñor, Lourdes , Miralles-Pechuán, Luis , González-Mendoza, Miguel

Martínez Villaseñor, M. de L., Miralles Pechuan, L. J. y González Mendoza, M. (2016). Interoperability in electronic health records through the mediation of ubiquitous user model. En: En: García, C, Caballero Gil, P., Burmester, M. y Quesada Arencibia, A. (editores), Ubiquitous Computing and Ambient Intelligence : 10th International Conference, UCAmI 2016, San Bartolomé de Tirajana, Gran Canaria, Spain, November 29 - December 2, 2016 (vol. 1), (pp. 190-200). Cham : Springer International Publishing. DOI: 10.1007/978-3-319-48746-5_19

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The Relevance of Cataract as a Risk Factor for Age-Related Macular Degeneration: A Machine Learning Approach

2019 , Martínez Velasco, Antonieta Teodora , Martinez-Villaseñor, Lourdes , Miralles-Pechuán, Luis , Pérez Ortiz, Andric Christopher , Zenteno, Juan Carlos , Estrada Mena, Francisco Javier

Age-related macular degeneration (AMD) is the leading cause of visual dysfunction and irreversible blindness in developed countries and a rising cause in underdeveloped countries. There is a current debate on whether or not cataracts are significant risk factors for AMD development. In particular, research regarding this association is so far inconclusive. For this reason, we aimed to employ here a machine-learning approach to analyze the relevance and importance of cataracts as a risk factor for AMD in a large cohort of Hispanics from Mexico. We conducted a nested case control study of 119 cataract cases and 137 healthy unmatched controls focusing on clinical data from electronic medical records. Additionally, we studied two single nucleotide polymorphisms in the CFH gene previously associated with the disease in various populations as positive control for our method. We next determined the most relevant variables and found the bivariate association between cataracts and AMD. Later, we used supervised machine-learning methods to replicate these findings without bias. To improve the interpretability, we detected the five most relevant features and displayed them using a bar graph and a rule-based tree. Our findings suggest that bilateral cataracts are not a significant risk factor for AMD development among Hispanics from Mexico. © 2019 by the authors.

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A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks

2016 , Ponce, Hiram , Miralles-Pechuán, Luis , Martinez-Villaseñor, Lourdes

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches.

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Human Activity Recognition on Mobile Devices Using Artificial Hydrocarbon Networks

2018 , Ponce, Hiram , Miralles-Pechuán, Luis , González Mora, José Guillermo , Martinez-Villaseñor, Lourdes

Human activity recognition (HAR) aims to classify and identify activities based on data-driven from different devices, such as sensors or cameras. Particularly, mobile devices have been used for this recognition task. However, versatility of users, location of smartphones, battery, processing and storage limitations, among other issues have been identified. In that sense, this paper presents a human activity recognition system based on artificial hydrocarbon networks. This technique have been proved to be very effective on HAR systems using wearable sensors, so the present work proposes to use this learning method with the information provided by the in-sensors of mobile devices. Preliminary results proved that artificial hydrocarbon networks might be used as an alternative for human activity recognition on mobile devices. In addition, a real dataset created for this work has been published. © Springer Nature Switzerland AG 2018.

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