Roberto Antonio Vázquez

Roberto Antonio Vázquez
Universidad La Salle México · Faculty of Engineering

PhD
Head of Research at Universidad La Salle México

About

90
Publications
24,276
Reads
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1,052
Citations
Additional affiliations
February 2010 - present
Universidad La Salle México
Position
  • Researcher
January 2010 - present
La Salle University
January 2009 - present
Instituto Politécnico Nacional (IPN)

Publications

Publications (90)
Article
Full-text available
Purpose The aim of this study was to evaluate the demographic characteristics, clinical and pathological factors, and the outcome of cancer and COVID-19 patients in Mexico. Patients and methods A prospective, multicentric study was performed through a digital platform to have a national registry of patients with cancer and positive SARS-CoV-2 test...
Article
Full-text available
Medical imaging has improved the diagnostic capability of many diseases, as well as surgical planning and monitoring of chronic degenerative diseases. This allows specialists to improve accuracy by applying image segmentation. Recent research proposes new techniques and algorithms to facilitate this work, reducing execution time and inter-intra ope...
Chapter
Evolutionary Algorithms (EAs) and other kind of metaheuristics are utilized to either design or optimize the architecture of Artificial Neural Networks (ANNs) in order to adapt them for solving a specific problem; these generated ANNs are known as Evolutionary Artificial Neural Networks (EANNs). Their architecture components, including number of ne...
Chapter
Deficient nutrition has caused high rates of overweight and obesity in the Mexican population, increasing the cases of people with diabetes and hypertension. In order to solve this, it is necessary to promote a change in the alimentation to reduce the rates of overweight and obesity. To achieve this, we propose a friendly solution to generate a cha...
Article
Brain signals classification is an interesting topic due its different applications not only in the medical field, but also in the development of technology. Due to the features of the brain signals such as high variability and complexity, the stages previous to the classification: pre-processing and feature extraction, are crucial. Particularly, t...
Chapter
Gene expression in DNA microarrays has been widely used to determine which genes are related with a disease, identify tumors, determine a treatment for a disease, etc.; all of this based on the classification of DNA microarrays. Several pattern recognition and computational intelligence techniques such as Artificial Neural Networks (ANN) have been...
Chapter
The overweight in the population has become a problem due to the deficiency on the nutritional contributions, increasing the number of people with diseases. The origin of this problem lies in the way people eat, with a poor nutritional quality and in excessive quantities. To solve this, it is necessary that people consider balance diets with the nu...
Article
Full-text available
In the development of a brain-computer interface (BCI), some issues should be regarded in order to improve its reliability and performance. Perhaps, one of the most challenging issues is related to the high variability of the brain signals, which directly impacts the accuracy of the classification. In this sense, novel feature extraction techniques...
Chapter
The DNA Microarray classification played an important role in bioinformatics and medicine area. By means of the genetic expressions obtained from a DNA microarrays, it is possible to identify which genes are correlated to a particular disease, in order solve different tasks such as tumor detection, best treatment selection, etc. In the last years,...
Article
Full-text available
Motor imagery-based brain–computer interfaces decode users’ intentions from the electroencephalogram; however, poor spatial resolution makes automatic recognition of these intentions a challenging task. New classification approaches with low computational costs and high classification performances need to be developed in order to increase the numbe...
Article
Since the emergence of brain computer interface (BCI), several methods have been applied to associate an electroencephalographic (EEG) recording with a specific mental task. Particularly, in the classification stage, several techniques such as linear Fisher discriminant (LD), feed-forward artificial neural networks (FNN) and radial basis function n...
Chapter
Several computational techniques have been proposed in the last years to classify brain signals in order to increase the performance of Brain-Computer Interfaces. However, there are several issues that should be attended to be more friendly with the users during the calibration stage and to achieve more reliable BCI applications. One of these issue...
Conference Paper
Identifying which crop is growing in certain areas is important to many national and multinational agricultural agencies for forecasting grain supplies, monitoring farming activity, facilitating crop rotation records, etc. In order to achieve that, the agencies require to schedule censuses on a regular basis. Recently, different techniques based on...
Conference Paper
Spiking neural networks (SNN) have been successfully applied in pattern classification problems. However, their performance for solving complex problems such as electroencephalography (EEG) classification has not been widely assessed. It is necessary to consider new approaches to select relevant information and for training SNN in order to improve...
Article
Motor imagery is a cognitive process which involves a planning of a movement without performing its execution. This planning can be decoded from the electroencephalography signal and be used for therapeutic purposes. Self-organized maps are artificial neural networks that have a potential to be used as motor imagery classifiers. In this work 11 cha...
Conference Paper
Face Recognition Systems has been applied in a wide range of applications. However, their efficiency drastically diminish when they are applied under uncontrolled environments such as illumination change conditions, face position and expressions changes. Because of that, it is necessary to evaluate the performance of different feature extraction te...
Article
DNA microarray is an efficient new technology that allows to analyze, at the same time, the expression level of millions of genes. The gene expression level indicates the synthesis of different messenger ribonucleic acid (mRNA) molecule in a cell. Using this gene expression level, it is possible to diagnose diseases, identify tumors, select the bes...
Article
Full-text available
Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Partic...
Article
Full-text available
Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern...
Conference Paper
DNA microarrays are a powerful technique in genetic science due to the possibility to analyze the gene expression level of millions of genes at the same time. Using this technique, it is possible to diagnose diseases, identify tumours, select the best treatment to resist illness, detect mutations and prognosis purpose. However, the main problem tha...
Conference Paper
Full-text available
Agricultural activities could represent an important sector for the economy of certain countries. In order to maintain control of this sector, it is necessary to schedule censuses on a regular basis, which represents an enormous cost. In recent years, different techniques have been proposed with the objective of reducing the cost and improving auto...
Chapter
Spiking neurons are neural models that try to simulate the behavior of biological neurons. This model generates a response (spikes or spike train) only when the model reaches a specific threshold. This response could be coded into a firing rate and perform a pattern classification task according to the firing rate generated with the input current....
Conference Paper
Full-text available
Most of the samples discovered are variations of known malicious programs and thus have similar structures, however, there is no method of malware classification that is completely effective. To address this issue, the approach proposed in this paper represents a malware in terms of a vector, in which each feature consists of the amount of APIs cal...
Conference Paper
Full-text available
Some spiking neuron models have proved to solve different linear and non-linear pattern recognition problems. Indeed, only one spiking neuron can generate comparable results as classical artificial neural network. However, depending on the classification problem, one spiking model could be better or less efficient than other. In this paper we propo...
Conference Paper
Full-text available
Spiking Neuron Models can accurately predict the spike trains produced by cortical neurons in response to somatically injected electric currents. Since the specific model characteristics depend on the neuron; a computational method is required to fit models to electrophysiological recordings. However, models only work within defined limits and it i...
Article
Full-text available
En el área de la Inteligencia Artificial, las Redes Neuronales Artificiales (RNA) han sido aplicadas para la solución de múltiples tareas. A pesar de su declive y del resurgimiento de su desarrollo y aplicación, su diseño se ha caracterizado por un mecanismo de prueba y error, el cual puede originar un desempeño bajo. Por otro lado, los algoritmos...
Conference Paper
Full-text available
In recent years, scientists and researchers have paid special attention to the implementation of Spiking Neural Networks (SNN), for approaching simulations of the human brain mechanisms, or to solve practical problems, such as epilepsy and seizure detection [1]. Nevertheless, large-scale SNN simulations are expensive from the computational point of...
Conference Paper
Full-text available
Natural phenomena such as earthquakes have caused devastating effects in different cities around the word. To prevent a great disaster, it is necessary to construct seismic stations at strategical locations to warn population. Many Disaster Alert Systems (DAS), such as the Seismic Alert System of Mexico City (SAS) [4] or the Deep-ocean Assessment a...
Article
Median associative memories (MED-AMs) are a special type of associative memory that substitutes the maximum and minimum operators of a morphological associative memory with the median operator. This associative model has been applied to restore grey scale images and provided a better performance than morphological associative memories when the patt...
Article
Full-text available
Bio-inspired algorithms have shown their usefulness in different non-linear optimization problems. Due to their efficiency and adaptability, these algorithms have been applied to a wide range of problems. In this paper we compare two ways of training an artificial neural network (ANN): Particle Swarm Optimization (PSO) algorithms against classical...
Article
Full-text available
Visual attention is a mechanism that biological systems have developed to reduce the large amount of visual information in order to efficiently perform tasks such as learning, recognition, tracking, etc. In this paper, we describe a simple spiking neural network model that is able to detect, focus on and track a stimulus even in the presence of noi...
Conference Paper
Due to their efficiency and adaptability, bio-inspired algorithms have shown their usefulness in a wide range of different non-linear optimization problems. In this paper, we compare two ways of training an artificial neural network (ANN): Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms. The main contribution of this pa...
Conference Paper
Full-text available
Meta-heuristic algorithms inspired by nature have been used in a wide range of optimization problems. These types of algorithms have gained popularity in the field of artificial neural networks (ANN). On the other hand, spiking neural networks are a new type of ANN that simulates the behaviour of a biological neural network in a more realistic mann...
Conference Paper
Full-text available
Several meta-heuristic algorithms have been pro posed in the last years for solving a wide range of optimization problems. Cuckoo Search Algorithm (CS) is a novel meta heuristic based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Levy flight behavior of some birds and fruit flies. This algorithm has been a...
Conference Paper
Artificial bee colony (ABC) algorithm has been used in several optimization problems, including the optimization of synaptic weights from an Artificial Neural Network (ANN). However, this is not enough to generate a robust ANN. For that reason, some authors have proposed methodologies based on so-called metaheuristics that automatically allow desig...
Conference Paper
Full-text available
Recent studies warn of a possible major earthquake off the coast of State of Guerrero, Mexico, so that, it turns important to alert the population as long as possible and avoid a great disaster. This requires the construction of a network of seismic sensing stations, located at strategical positions, to detect earthquakes and issue a timely warning...
Conference Paper
Full-text available
Artificial bee colony (ABC) algorithm has been used in several optimization problems, including the optimization of synaptic weights from an Artificial Neural Network (ANN). However, this is not enough to generate a robust ANN. For that reason, some authors have proposed methodologies based on so-called metaheuristics that automatically allow desig...
Conference Paper
The design of an Artificial Neural Network (ANN) is a difficult task for it depends on the human experience. Moreover it needs a process of testing and error to select which kind of a transfer function and which algorithm should be used to adjusting the synaptic weights in order to solve a specific problem. In the last years, bio-inspired algorithm...
Conference Paper
Full-text available
In this paper, it is shown how a Leaky Integrate and Fire (LIF) neuron can be applied to solve non-linear pattern recognition problems. Given a set of input patterns belonging to K classes, each input pattern is transformed into an input signal, then the LIF neuron is stimulated during T ms and finally the firing rate is computed. After adjusting t...
Conference Paper
Full-text available
Median associative memories (MED-AMs) are a special type of associative memory based on the median operator. This type of associative model has been applied to the restoration of gray scale images and provides better performance than other models, such as morphological associative memories, when the patterns are altered with mixed noise. Despite of...
Conference Paper
Full-text available
Visual attention is a powerful mechanism that enables perception to focus on a small subset of the information picked up by our eyes. It is directly related to the accuracy of an object categorization task. In this paper we adopt those biological hypotheses and propose an evolutionary visual attention model applied to the face recognition problem....
Article
Full-text available
A view-based method for D object recognition based on some biological aspects of infant vision is proposed in this paper. The biological hypotheses of this method are based on the role of the response to low frequencies at early stages as well as some conjectures concerning how an infant detects subtle features (stimulating points) from an object....
Conference Paper
Full-text available
Different varieties of artificial neural networks have proved their power in several pattern recognition problems, particularly feed-forward neural networks. Nevertheless, these kinds of neural networks require of several neurons and layers in order to success when they are applied to solve non-linear problems. In this paper is shown how a spiking...
Article
Full-text available
In this paper is shown how an Izhikevich neuron can be applied to solve different linear and non-linear pattern recognition problems. Given a set of input patterns belonging to K classes, each input pattern is transformed into an input signal, then the Izhikevich neuron is stimulated during T ms and finally the firing rate is computed. After adjust...
Article
Morphological associative memories (MAMs) are a special type of associative memory which exhibit optimal absolute storage capacity and one-step convergence. This associative model substitutes the additions and multiplications used by other models by computing maximums and minimums. This type of associative model has been applied to different patter...
Conference Paper
Full-text available
Recently, it was shown how some metaphors, adopted from the infant vision system, were useful for face recognition. In this paper we adopt those biological hypotheses and apply them to the 3D object recognition problem. As the infant vision responds to low frequencies of the signal, a low-filter is used to remove high frequency components from the...
Article
Full-text available
Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Partic...
Conference Paper
Morphological associative memories (MAMs) are a special type of associative memory which exhibit optimal absolute storage capacity and one-step convergence. This associative model substitutes the additions and multiplications used in the classical models by additions/subtractions and maximums/minimums depending on the proposed model. MAMs have been...
Conference Paper
Morphological associative memories (MAMs) are a special type of associative memory which exhibit optimal absolute storage capacity and one-step convergence. This associative model substitutes the additions and multiplications by additions/subtractions and maximums/minimums. This type of associative model has been applied to different pattern recogn...
Conference Paper
Full-text available
Morphological associative memories (MAMs) are a special type of associative memory which exhibit optimal absolute storage capacity and one-step convergence. This associative model substitutes the additions and multiplications used in the classical models by additions/subtractions and maximums/minimums depending on the proposed model. MAMs have been...
Article
Full-text available
An associative memory (AM) is a special kind of neural network that allows associating an output pattern with an input pattern. Some problems require associating several output patterns with a unique pattern. Classical associative and neural models cannot solve this simple task and less if these patterns are complex images, for example faces. In th...
Article
The brain is not a huge fixed neural network, but a dynamic, changing neural network that continuously adapts to meet the demands of communication and computational needs. In classical neural networks approaches, particularly associative memory models, synapses are only adjusted during the training phase. After this phase, synapses are no longer a...
Article
Full-text available
Classical bidirectional associative memories (BAM) have poor memory storage capacity, are sensitive to noise, are subject to spurious steady states during recall, and can only recall bipolar patterns. In this paper, we introduce a new bidirectional hetero-associative memory model for true-color patterns that uses the associative model with dynamica...
Conference Paper
An associative memory is a particular type of neural network for recalling output patterns from input patterns that might be altered by noise. During the last 50 years, several associative models have emerged and they only have been applied to solve problems where input patterns are images. Most of these models have several constraints that limit t...
Conference Paper
An associative memory (AM) is a special kind of neural network that allows associating an output pattern with an input pattern. In the last years, several associative models have been proposed by different authors. However, they have several constraints which limit their applicability in complex pattern recognition problems. In this paper we gather...
Conference Paper
Full-text available
Several associative memories (AM) have been proposed in the last years. These AMs have several constraints that limit their applicability in com- plex problems such as face recognition. Despite of the power of these models, they cannot reach its full power without applying new mechanisms based on current and future studies on biological neural netw...
Conference Paper
Full-text available
In this paper we propose a view-based method for 3D object recogni- tion based on some biological aspects of infant vision. The biological hypothe- ses of this method are based on the role of the response to low frequencies at early stages, and some conjectures concerning how an infant detects subtle fea- tures (stimulating points) from an object....
Conference Paper
Full-text available
Path planning is one of the problems in robotics. It consists on automatically determine a path from an initial position of the robot to its final position. In this paper we propose a variant of the ant colony system (ACO) applied to optimize the path that a robot can follow to reach its target destination. We also propose to evolve some parameters...
Conference Paper
In this paper we show how a simplified version of a describing vector can be used to efficiently recognize complex objects. We describe how simplified vectors are randomly obtained from complete describing vectors and how these simplified versions can be used to recognize faces. We compare the efficiency of the proposal against PCA using several kn...
Conference Paper
In this paper we show how a simplified version of a describing vector can be used to efficiently recognize complex objects. We describe how simplified vectors are randomly obtained from complete describing vectors and how these simplified versions can be used to recognize faces. We compare the efficiency of the proposal against PCA using several kn...
Conference Paper
A novel method for face recognition based on some biological aspects of infant vision is proposed in this paper. The biological hypotheses of this method are based on the role of the response to low frequencies at early stages, and some conjectures concerning how an infant detects subtle features (stimulating points) from a face. In order to recogn...
Conference Paper
In this paper we study how the performance of a median associative memory is influenced when the values of its elements are altered by noise. To our knowledge this kind of research has not been reported until know. We give formal conditions under which the memory is still able to correctly recall a pattern of the fundamental set of patterns either...
Article
Full-text available
F., hsossa@cic.ipn.mx Most research in pattern recognition is focused on statistical, syntactical, combinatorial, neural and fuzzy approaches. Despite of their robustness in complex problems, they cannot reach their full power until new mechanisms based on current and future study of neural processes on the human brain are applied. For example, ver...
Article
Full-text available
An important problem in image management is image retrieval. When im-ages in a database are not well organized, their efficient retrieval is a problem. In this paper we describe an image retrieval system able to recover a set of images that sat-isfy some searching criteria. The proposal works well even if the objects appearing in the images are occ...
Article
Most of the Neural Network models proposed during the last few years are capable of solving several complex problems such as recognition, forecast or reconstruction of different phenomena. A crucial feature of these models is that they tackle a wide variety of classification problems, and although these models work accurately within a limited parti...
Conference Paper
Full-text available
Path Planning is one of the problems in robotics. It consists on automatically determine a path from an initial position of the robot to its final position. This process has to be adaptable in order to work over several environments. Bio-inspirited algorithms, particularly ant colony optimizations (ACO) [9] are adaptable to any environments. Taking...