Antonio Hernández Zavala

23399140600

Publications - 3

Classification of plantar foot alterations by fuzzy cognitive maps against multi-layer perceptron neural network

F. Hatwágner Antonio Hernández Zavala Silvia L. Chaparro-Cardenas Julian Andres Ramirez-Bautista L. T. Koczy Jorge A. Huerta-Ruelas

Publication Name: Biocybernetics and Biomedical Engineering

Publication Date: 2020-01-01

Volume: 40

Issue: 1

Page Range: 404-414

Description:

Load distribution analysis on foot surface allows knowing human mechanical behavior and aids the doctor in the detection of gait disorders like, the risk of foot ulcerations, leg discrepancy, and footprint alterations. Plantar pressure data combined with techniques that use integral reasoning produce easy understanding medical tools for assisting in treatment, early detection, and the development of preventive strategies. The present research compares the classification of human plantar foot alterations using Fuzzy Cognitive Maps (FCM) trained by Genetic Algorithm (GA) against a Multi-Layer Perceptron Neural Network (MLPNN). One hundred and fifty-one subject volunteers (aged 7–77) were classified previously with the flat foot (n = 70) and cavus foot (n = 81) by specialized physicians of the Piédica diagnostic center. The trial walking was conducted using plantar pressure platforms FreeMed®. The foot surface was divided into 14 areas that included toe 1 st to 5th, metatarsal joint 1st to 5th, lateral midfoot, medial midfoot, lateral heel, and medial heel. Pressure data were normalized for each area. Better performance in the classification using small amounts of data were found by using Fuzzy rather than non-Fuzzy approach.

Open Access: Yes

DOI: 10.1016/j.bbe.2019.12.008

Detection of Human Footprint Alterations by Fuzzy Cognitive Maps Trained with Genetic Algorithm

F. Hatwágner Antonio Hernández Zavala Silvia L. Chaparro-Cardenas Julian Andres Ramirez-Bautista L. T. Koczy Jorge A. Huerta-Ruelas

Publication Name: Proceedings of the Special Session 2018 17th Mexican International Conference on Artificial Intelligence Micai 2018

Publication Date: 2018-10-01

Volume: Unknown

Issue: Unknown

Page Range: 32-38

Description:

Mobility is an important part of our daily life, hence the good health of our lower extremities is essential. Gait analysis using kinetic data along with medical Decision Support System or Computer Aided Diagnosis provide to physicians support in gait disorder detection, the risk of foot ulcerations especially in diabetic patients, leg discrepancy, footprint pathologies, and many other applications in biomedical diagnosis. To increase confidence in the system, it is necessary to use a technique which uses a comprehensive reasoning and provide explanations to discover new relationships and combination of features. The present research is an attempt to assess the viability of investigating human footprint alterations using Fuzzy Cognitive Maps (FCM) combined with a Genetic Algorithm (GA), and it is part for preparation of investigating more efficient algorithms in the future. In the proposed method, GA is used to learn the weight matrix of an FCM model applied to identify alterations in the human footprint. Using historical plantar pressure data obtained by electronic platforms, combined with FCM and optimization algorithm, a promising outcome is presented in the field of Computer-Aided Diagnosis.

Open Access: Yes

DOI: 10.1109/MICAI46078.2018.00013

Hardware implementation of fuzzy flip-flops based on łukasiewicz norms

Antonio Hernández Zavala László Gál Rita Lovassy Ildar Batyrshin Oscar Camacho Nieto L. T. Koczy

Publication Name: Proceedings of the 9th Wseas International Conference on Applied Computer and Applied Computational Science Acacos 10

Publication Date: 2010-12-01

Volume: Unknown

Issue: Unknown

Page Range: 196-201

Description:

The digital hardware implementation of various fuzzy operations furthermore of fuzzy flip-flops has been the subject of intense study and application. The fuzzy D flip-flop derived from fuzzy J-K one is a single input - single output unit with sigmoid transfer characteristics in some particular cases, proper to use as neuron in a Fuzzy Neural Networks (FNN). In this paper we propose the hardware realization of fuzzy D flip-flops based on Łukasiewicz norms.

Open Access: Yes

DOI: DOI not available