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 19
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Publication

An ontology driven multi-agent system for client assignment in a bank queue

2010 , Martinez-Villaseñor, Lourdes , González-Marrón, David , González-Mendoza, Miguel , Hernández Gress, Neil

This paper presents an ontology driven multi-agent system that uses a negotiation process for decision support in a Bak Queue. The system assists queue client assignment based on the client profile and the cashiers’ workload in order to guarantee a minimum time response in client attention.

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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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Analysis of Contextual Sensors for Fall Detection

2019 , Martinez-Villaseñor, Lourdes , Ponce, Hiram

Falls are a major problem among older people and often cause serious injuries. It is important to have efficient fall detection solutions to reduce the time in which a person who suffered a fall receives assistance. Given the recent availability of cameras, wearable and ambient sensors, more research in fall detection is focused on combining different data modalities. In order to determine the positive effects of each modality and combination to improve the effectiveness of fall detection, a detailed assessment has to be done. In this paper, we analyzed different combinations of wearable devices, namely IMUs and EEG helmet, with grid of active infrared sensors for fall detection, with the aim to determine the positive effects of contextual information on the accuracy in fall detection. We used short-term memory (LSTM) networks to enable fall detection from sensors raw data. For some activities certain combinations can be helpful to discriminate other activities of daily living (ADL) from falls. © 2019 IEEE.

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Interpretability of artificial hydrocarbon networks for breast cancer classification

2017 , Ponce, Hiram , Martinez-Villaseñor, Lourdes

This special issue of the journal Soft Computing offers extended versions of some of the best-awarded, high-reviewed and invited papers presented on the 13th Mexican International Conference on Artificial Intelligence, MICAI 2014, held in Tuxtla Gutiérrez, Chiapas, Mexico, on November 16–22, 2014, under the organization of the Mexican Society for Artificial Intelligence (SMIA) in cooperation with the Instituto Tecnológico de Tuxtla Gutiérrez and Universidad Autónoma de Chiapas, and on the 14th Mexican International Conference on Artificial Intelligence, MICAI 2015, held in Cuernavaca, Morelos, Mexico, on October 25–31, 2015, under the organization of the SMIA in cooperation with the Instituto de Investigaciones Eléctricas. ©2017 Soft Computing, Springer Verlag.

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Towards an ontology for ubiquitous user modeling interoperability

2012 , Martinez-Villaseñor, Lourdes , González-Mendoza, Miguel

In order to obtain a broader understanding of the user, some researchers in the community of user modeling envision the need to share information of user models between applications. But gathering distributed user information from heterogeneous sources to obtain user models interoperability implies handling syntactic and semantic heterogeneity. It is also important to provide means for a ubiquitous user model to evolve over time. We present U2MIO a dynamic ontology with flexible structure for user modeling interoperability based in SKOS ontology. The U2MIO provides mediation based user modeling for sharing and reusing information from heterogeneous user models. A two-tier matching strategy is proposed for the process of concept alignment that permits the interoperability between profile suppliers and consumers.

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A Method to Improve Speed of Training Algorithm in Artificial Hydrocarbon Networks

2019 , Campos Souza, Paulo V. de , Ponce, Hiram , Martinez-Villaseñor, Lourdes

Artificial hydrocarbon networks (AHN) is a supervised machine learning method inspired on chemical carbon networks that simulate heuristic chemical rules involved within organic molecules to represent the structure and behavior of data. However, training AHN depends on a relevant number of parameters. In that sense, the original training algorithm presents some issues to find suitable parameters in a reasonable amount of time. Thus, this paper proposes a new training algorithm for AHN based on the concept of extreme learning machines, to update weight parameters related to the molecular functions. To evaluate the effectiveness of the proposed algorithm, binary classification and regression tests are performed over real public datasets from a central data repository specialized in machine learning problems. The results obtained validated that the updating of the weight parameters using the new training algorithm in the molecular structures is efficient and maintains the expected results of model accuracy. In addition, this work increased up to 24.88% the speed of the training phase in contrast to the original algorithm. © 2019 IEEE.

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Design of a Non-Actuator Soft Gripper for a Chameleon-Like Robot

2021 , Ponce, Hiram , Martinez-Villaseñor, Lourdes , Mayorga Acosta, Carlos

Four-legged robots are terrestrial mobile robots widely applied in tasks of navigation that imply complex mobility, the difficulty of obstacle avoidance, efficient energy management, or handling the speed of motion. In previous work, we designed and implemented a prototype of a robot inspired by the biomechanics of the chameleon, for future applications in rescue and maintenance. But, the legs of the prototype slip into the contact surface, resulting in the diminishing of the locomotion performance. Hence, it is necessary to add a gripper in the tip of the legs, like a prehensile hand, for avoiding relative sliding between the legs and the surface. Thus, in this paper, we propose the first soft gripper designed for the chameleon-like robot. The key feature of the gripper is its activation without any actuator due to size restrictions and the prevention of using pneumatic or hydraulic actuators. To validate the proposal, we simulate the gripper and we run a finite element analysis, providing us insights into the soft gripper model. © 2021 IEEE.

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Challenges in Data Acquisition Systems: Lessons Learned from Fall Detection to Nanosensors

2018 , Peñafort Asturiano, Carlos J. , Santiago, Nestor , Nuñez-Martínez, José , Ponce, Hiram , Martinez-Villaseñor, Lourdes

Falls are a major public health problem in elderly people often causing fatal injuries. It is important to assure that injured people receive assistance as quick as possible. Fall detection systems have gain more relevance nowadays. As more databases and fall detection systems are developed, there is more need to identify the challenges encountered in building and creating them. This paper addresses pre-processing, inconsistency and synchronization challenges that occur when creating a multimodal database for fall detection. We present different algorithms used to tackle these issues. We describe the issues and the corresponding solutions in order to document the lessons learned that could help others in data acquisition for multimodal databases. Applying the solutions to the issues found so far, we acquired an organized multimodal database for fall detection with 17 subjects. Furthermore, these lessons learned can be applied for data nanosensors acquisition and storage. © 2018 IEEE.

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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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Comparative Analysis of Artificial Hydrocarbon Networks versus Convolutional Neural Networks in Human Activity Recognition

2020 , Ponce, Hiram , Martinez-Villaseñor, Lourdes

Human activity recognition (HAR) has gained interest in the research communities in order to know the behavior and context of users for medical, sports performance evaluation, ambient assisted living and security applications. Recent works suggest that convolutional neural networks (CNN) are very competitive machine learning techniques for HAR. Nevertheless, CNN require many computational resources, high number of parameter tuning, and many data samples for training. In this paper, we present a comparative analysis of a novel technique, artificial hydrocarbon networks (AHN), with CNN on human activity recognition classification task. We choose to compare AHN with CNN given that it is a very well-suited machine learning technique for HAR. We show that AHN architecture is simpler to set up than CNN, it needs less hyper-parameter configuration and has a slightly better accuracy performance. © 2020 IEEE.

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