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 23
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Ethical Design Framework for Artificial Intelligence Healthcare Technologies

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

The healthcare industry has undergone a profound transformation with the integration of artificial intelligence(AI) and emerging technologies, leading in a new era of personalized treatments and healthcare solutions. However, this technological advancement has not been without its ethical and practical challenges, which have hindered the real-world application of intelligent systems in clinical settings. Numerous international entities have published principles, guidelines, and regulations to tackle these issues, yet a significant gap persists between theoretical initiatives and the practical incorporation of ethical design in intelligent systems. Within this study, we delineate the substantial transformation taking place in the healthcare landscape due to artificial intelligence. We provide a condensed overview of the opportunities and challenges that accompany this disruptive shift. The main goal of this work is introducing an ethical design framework tailored to healthcare technologies and delineating the ethical design process for a machine learning-based support tool designed for Age-related Macular Degeneration risk assessment. To the best of our knowledge, this work represents one of the few documented instances of the practical implementation of ethical design principles in intelligent healthcare systems. © 2024 Springer Nature

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Versatility of Artificial Hydrocarbon Networks for Supervised Learning

2018 , Ponce, Hiram , Martinez-Villaseñor, Lourdes

Surveys on supervised machine show that each technique has strengths and weaknesses that make each of them more suitable for a particular domain or learning task. No technique is capable to tackle every supervised learning task, and it is difficult to comply with all possible desirable features of each particular domain. However, it is important that a new technique comply with the most requirements and desirable features of as many domains and learning tasks as possible. In this paper, we presented artificial hydrocarbon networks (AHN) as versatile and efficient supervised learning method. We determined the ability of AHN to solve different problem domains, with different data-sources and to learn different tasks. The analysis considered six applications in which AHN was successfully applied. © Springer Nature Switzerland AG 2018.

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Advances in soft computing : 18th Mexican International Conference on Artificial Intelligence, MICAI 2019, Xalapa, Mexico, October 27 - November 2, 2019, Proceedings : Preface

2019 , Martinez-Villaseñor, Lourdes

The Mexican International Conference on Artificial Intelligence (MICAI) is a yearly international conference series that has been organized by the Mexican Society of 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. The proceedings of MICAI 2019 contains 59 papers structured into four sections: Machine Learning, Fuzzy Systems, Reasoning, and Intelligent Applications, Computer Vision and Robotics, Optimization and Planning This book should be of interest to researchers in all fields of AI, students specializing in related topics, and for the public in general interested in recent developments in AI. ©Springer Nature Switzerland AG 2019.

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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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Preface : Advances in Computational Intelligence : 22nd Mexican International Conference on Artificial Intelligence, MICAI 2023, Yucatán, Mexico, November 13–18, 2023, Proceedings, Part I

2024-01-01 , Calvo, Hiram , Martinez-Villaseñor, Lourdes , Ponce, Hiram , Zatarain-Cabada, Ramón , Montes Rivera, Martín , Mezura-Montes, Efrén

Conference proceedings front matter may contain various advertisements, welcome messages, committee or program information, and other miscellaneous conference information. This may in some cases also include the cover art, table of contents, copyright statements, title-page or half title-pages, blank pages, venue maps or other general information relating to the conference that was part of the original conference proceedings.

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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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Mass Segmentation in Digital Mammograms

2015 , Carreras Cruz, María Victoria , Martinez-Villaseñor, Lourdes , Rosas-Pérez, Kevin Nataniel

Digital mammograms are among the most difficult medical images to read, because of the differences in the types of tissues and their low contrasts. This paper proposes a computer aided diagnostic system for mammographic mass detection that can distinguish between tumorous and healthy tissue among various parenchymal tissue patterns. This method consists in extraction of regions of interest, noise elimination, global contrast improvement, combined segmentation, and rule-based classification. The evaluation of the proposed methodology is carried out on Mammography Image Analysis Society (MIAS) dataset. The achieved results increased the detection accuracy of the lesions and reduced the number of false diagnoses of mammograms.

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Approaching Fall Classification Using the UP-Fall Detection Dataset: Analysis and Results from an International Competition

2020 , Ponce, Hiram , Martinez-Villaseñor, Lourdes

This chapter presents the results of the Challenge UP – Multimodal Fall Detection competition that was held during the 2019 International Joint Conference on Neural Networks (IJCNN 2019). This competition lies on the fall classification problem, and it aims to classify eleven human activities (i.e. five types of falls and six simple daily activities) using the joint information from different wearables, ambient sensors and video recordings, stored in a given dataset. After five months of competition, three winners and one honorific mention were awarded during the conference event. The machine learning model from the first place scored$$82.47\%$$ in$$F:1$$-score, outperforming the baseline of$$70.44\%$$. After analyzing the implementations from the participants, we summarized the insights and trends of fall classification. © 2020, Springer Nature Switzerland AG.

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Fuzzy-Based Approach of Concept Alignment

2017 , Martinez-Villaseñor, Lourdes , González-Mendoza, Miguel

The need to share and reuse information has grown in the new era of Internet of things and ubiquitous computing. Researchers in ontology and schema matching use mapping approaches in order to achieve interoperability between heterogeneous sources. The use of multiple similarity measures that take into account lexical, structural and semantic properties of the concepts is often found in schema matching for the purpose of data integration, sharing and reusing. Mappings identified by automatic or semi-automatic tools can never be certain. In this paper, we present a fuzzy-based approach to combine different similarity measures to deal with scenarios where ambiguity of terms hinder the process of alignment and add uncertainty to the match. © 2017, Springer International Publishing AG.

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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

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

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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