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
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A Reinforcement Learning Method for Continuous Domains Using Artificial Hydrocarbon Networks

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

Reinforcement learning in continuous states and actions has been limitedly studied in ocassions given difficulties in the determination of the transition function, lack of performance in continuous-to-discrete relaxation problems, among others. For instance, real-world problems, e.g. Fobotics, require these methods for learning complex tasks. Thus, in this paper, we propose a method for reinforcement learning with continuous states and actions using a model-based approach learned with artificial hydrocarbon networks (AHN). The proposed method considers modeling the dynamics of the continuous task with the supervised AHN method. Initial random rollouts and posterior data collection from policy evaluation improve the training of the AHN-based dynamics model. Preliminary results over the well-known mountain car task showed that artificial hydrocarbon networks can contribute to model-based approaches in continuous RL problems in both estimation efficiency (0.0012 in root mean squared-error) and sub-optimal policy convergence (reached in 357 steps), in just 5 trials over a parameter space θin R86. Data from experimental results are available at: http://sites.google.com/up.edu.mx/reinforcement-learning/ ©2018 IEEE.

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Artificial hydrocarbon networks for freezing of gait detection in Parkinson’s disease

2020 , Martinez-Villaseñor, Lourdes , Ponce, Hiram , Nuñez Martínez, José Pablo

Freezing of gait (FoG) is one of the most impairing phenomenon experienced by Parkinson's disease (PD) patients. This phenomenon is associated with falls and is an important factor that limits autonomy and impairs quality of life of PD patients. Pharmacological treatment is difficult and do not always help to deal with this problem. Robust FoG detection systems can help monitoring and identifying when a patient needs aid providing external cueing to deal with FoG episodes. In this paper, we describe a comparative analysis of traditional machine learning techniques against Artificial Hydrocarbon Networks (AHN) for FoG detection. We compared four supervised machine learning classifiers and AHN for FoG event detection using a publicly available dataset, obtaining 88% of F-score metric with AHN. We prove that AHN are suitable for FoG detection. © 2020 IEEE.

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An Explainable Tool to Support Age-related Macular Degeneration Diagnosis

2022 , Martinez-Villaseñor, Lourdes , Miralles-Pechuán, Luis , Ponce, Hiram , Martínez Velasco, Antonieta Teodora

Artificial intelligence and deep learning, in particu-lar, have gained large attention in the ophthalmology community due to the possibility of processing large amounts of data and dig-itized ocular images. Intelligent systems are developed to support the diagnosis and treatment of a number of ophthalmic diseases such as age-related macular degeneration (AMD), glaucoma and retinopathy of prematurity. Hence, explainability is necessary to gain trust and therefore the adoption of these critical decision support systems. Visual explanations have been proposed for AMD diagnosis only when optical coherence tomography (OCT) images are used, but interpretability using other inputs (i.e. data point-based features) for AMD diagnosis is rather limited. In this paper, we propose a practical tool to support AMD diagnosis based on Artificial Hydrocarbon Networks (AHN) with different kinds of input data such as demographic characteristics, features known as risk factors for AMD, and genetic variants obtained from DNA genotyping. The proposed explainer, namely eXplainable Artificial Hydrocarbon Networks (XAHN) is able to get global and local interpretations of the AHN model. An explainability assessment of the XAHN explainer was applied to clinicians for getting feedback from the tool. We consider the XAHN explainer tool will be beneficial to support expert clinicians in AMD diagnosis, especially where input data are not visual. © 2022 IEEE.

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