Ansel Y. Rodríguez GonzálezCONACYT - CICESE UT3
Ansel Y. Rodríguez González
PhD
CONACYT researcher at CICESE UT3. National Researcher Level I.
About
68
Publications
7,090
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
262
Citations
Citations since 2017
Introduction
Ansel Y. Rodríguez González has experience in software projects and research. His training in mathematics and computer science areas such as optimization, artificial intelligence, machine learning, data mining and pattern recognition; joined with knowledge of the technologies have enabled him to combine theoretical research with applied research and provide a high level of added value to the developments in which he has collaborated.
Additional affiliations
May 2018 - present
Centro de Investigación Científica y de Educación Superior de Ensenada, Baja California - Unidad de Transferencia Tecnologica Tepic
Position
- Researcher
August 2014 - May 2018
October 2011 - July 2014
Education
January 2008 - March 2011
September 2004 - July 2007
September 1999 - July 2004
Publications
Publications (68)
Destination Image can be considered as both, a theoretical and practical tool, to better understand how a destination is perceived in the minds of potential visitors. Given the im- pressive growth of digital sources of tourism-related data in the last decades, methods that exploit this information have been designed to explore this construct. Due t...
Data mining is an area of human knowledge that has drawn the attention of the scientific, academic, industrial, health, or business community in the last two decades. It has taken significant relevance due to all the contributions and applications throughout the main disciplines of knowledge such as medicine, tourism, education, or the military. Th...
The automatic classification of traffic signs is a tool to support drivers to have a safer driving when they are behind the wheel and even to assist the semi-autonomous driving of a vehicle. The objective of this work is to present a model capable and classify vandalized traffic signs using convolutional neural networks (CNN). The methodology used...
In Mexico, the highest percentage of people with Diabetes corresponds to women between 20 and 70 years old according to the National Institute of Statistics and Geography (INEGI) in 2018 [2]. Currently through technology it is important to provide material to reduce the percentage of Diabetes Mellitus (DM) in Mexico, therefore, in this work, an exp...
The social networks and the rapid development of new technologies have led to considerable changes in the tourism industry. Artificial intelligence, in particular natural language process ing (NLP), presupposes a significant advantage in obtaining information on the mass content
generated by online users concerning tourism services and products. T...
La sustentabilidad es "un proceso" que busca encontrar el equilibrio entre el medio ambiente y el uso de los recursos naturales. Mejorar la eficiencia de los procesos es vital en la disminución de residuos y en general, reducir el impacto al medio ambiente. Para alcanzar los objeti-vos de la ONU, necesitamos información estratégica para la toma opo...
This paper presents the framework and results from the Rest-Mex task at IberLEF 2022. This task considered three tracks: Recommendation System, Sentiment Analysis and Covid Semaphore Prediction, using texts from Mexican touristic places. The Recommendation System task consists in predicting the degree of satisfaction that a tourist may have when re...
This work aims to generate classification models that help determine the colour of an epidemiological semaphore (ES) by analysing online news and being better prepared for the different changes in the evolution of the pandemic. To accomplish this, we introduce Cov-NES-Mex corpus, a collection of 77,983 news (labelled with the Mexican ES system) rel...
Evolutionary computation is attracting attention in the energy domain as an alternative to tackle inherent mathematical complexity of some problems related to high-dimensionality, non-linearity, non-convexity, multimodality, or discontinuity of the search space. In this context, the research community launched the 2020 ”Competition on Evolutionary...
This study presents an analysis of the Rest-Mex forum task 2021, which is the first international evaluation event using tourism-related (Online Travels Reviews - OTRs) data from Mexico. In that forum, 14 specialized sentiment analysis systems were presented. The main contribution of this research is a method to successfully combine those 14 system...
El desperdicio de energía eléctrica utilizada en iluminación, provocado por el control manual de las luminarias es un problema que no solo afecta en el aspecto económico pues también afecta al consumidor, aunque es un problema estudiado alrededor del mundo, en México se ha tratado poco. Por ello, el presente trabajo tiene como objetivo desarrollar...
This paper presents the framework and results from the Rest-Mex track at IberLEF 2021. This track considered two tasks: Recommendation System and Sentiment Analysis, using texts from Mexican touristic places. The Recommendation System task consists in predicting the degree of satisfaction that a tourist may have when recommending a destination of N...
The management of today’s smart grids is a challenge due to the stochastic nature of renewable energies, the vehicle-to-grid functionalities, the participation of external providers and the bidding in local and external markets. For this reason, the “Competition on Evolutionary Computation in the Energy Domain: Smart Grid Applications” with a frame...
Fishing is a crucial worldwide activity as it provides a source of food and economic income. A challenge in ecology and conservation is decreasing overfishing and illegal, unreported, and unregulated fishing (IUUF). One strategy to decrease those issues is to track vessels for detecting fishing behaviors through monitory systems. In this letter, we...
The Energy Resource Management (ERM) can be modeled as a Mixed-Integer Non-Linear Problem whose aim is to maximize profits generally using smart grid capabilities more than importing energy from external markets. Due to this, many resources and customers are involved in optimization, making ERM a complex problem. Moreover, when the inherent uncerta...
Era marzo de 2020, se acrecentaba en las ciudades la ebullición de ideas y, muy especialmente, de profesio- nales y estudiantes, decididos a contribuir y aportar soluciones innovadoras que impactaran en las problemáticas ocasionadas por la propagación del COVID-19 en el país. En este contexto nació la iniciativa “México Hackea la Pandemia” buscando...
In this paper, a new Cellular Estimation Bayesian Algorithm for discrete optimization problems is presented. This class of
stochastic optimization algorithm with learning from the structure and parameters of local populations are based on
independence test and decentralized populations scheme, which can reduce the number of function evaluations s...
Association rule mining is one of the most important and active research areas in data mining. In the literature, several association rule miners have been proposed; among them, those based on particle swarm optimization (PSO) have reported the best results. However, these algorithms tend to prematurely fall into local solutions, avoiding a wide ex...
Objective: To apply the vehicle routing model based on optimized decisionmaking for the distribution of medical resources to in-patients, and patients with a possible COVID-19 diagnosis, in Camagüey, Cuba.
Methods: Heterogeneous vehicle routing problems with time windows were used in combination with optimization algorithms to cope with the distrib...
Objetivo:
Aplicar el modelo de enrutamiento de vehículos combinado con algoritmos de optimización para la toma de decisiones en la distribución de insumos relacionados con el servicio asistencial a pacientes hospitalizados y sospechosos de la COVID-19 en Camagüey, Cuba.
Métodos:
Se utilizaron los problemas de enrutamiento de vehículos heterogéneos...
Most frequent pattern mining algorithms assume that two sub-descriptions of instances are similar if and only if they are equals. However, other similarity functions are used in the soft sciences. In fact, some algorithms find patterns using similarity functions other than equality, but those algorithms are shown difficulties for mining datasets th...
In this research, a class of evolutionary algorithms (EA), called Cellular Estimation of Distribution Algorithms (CEDA) are proposed for discrete and continuous optimization problems. The main intention is to study the efficiency of EDA from two perspectives: to decrease the number of objective function evaluations (evaluative efficiency) and the e...
En este artículo se presenta a COLDPower: el agente inteligente mexicano de mercadeo de energía, que en diferentes versiones ha competido en el torneo anual Po- werTAC desde 2013. Se describe su arquitectura general, los agentes expertos en diferentes mercados y tipos de clientes que lo componen y cómo estos agentes apren- den. El 2do lugar alcanza...
Objective: The objective of this work is the application of the vehicle routing problem and its solution with optimization algorithms to the distribution of resources related to the COVID-19 treatment in Camagüey, Cuba, allowing decision making by health managers.
Methods: Heterogeneous vehicle routing problems with time windows were used, combine...
The Energy Resource Management (ERM) under uncertainty of a
smartgrid is a highly complex problem (a Mixed Integer Non-Linear
Problem) with the objective of maximizing profits by reducing the
need to buy energy from the day-ahead market or external suppliers
at high prices. In this paper, some approaches of Hybrid Estimation
of Distribution Algorit...
New Information and Communications Technologies (ICT), such as the Internet of Things (IoT), are enabling the evolution of energy grids towards a sophisticated power network called Smart Grid (SG). In the context of SGs, a microgrid is a self-sustained network that can operate in both grid-connected or stand-along modes. The long-term scheduling of...
![Figure 1: Major elements of the Power TAC scenario [25].](profile/Fernando-Lezama/publication/332844981/figure/fig1/AS:986798268375041@1612282449936/Major-elements-of-the-Power-TAC-scenario-25_Q320.jpg)
Free and competitive energy markets are a recent and increasing phenomenon in several countries. Understanding these new energy markets and estimating their possible evolutions are current challenges of the research community. To avoid real market risks, the research community have developed autonomous traders and tested them in the Power Trading A...
![Fig. 1. COLDPower architecture. [7]](profile/Ansel-Rodriguez-Gonzalez-2/publication/333984354/figure/fig1/AS:949830109118469@1603468553945/COLDPower-architecture-7_Q320.jpg)
The development of autonomous brokers that can learn effective strategies by combining the accumulated experience with the exploration of new courses of action becomes relevant with the changes in the energy matrix of the countries. In this paper, we present an overview of the autonomous broker COLDPower 2019. It was tested in the Qualification Rou...
One step in the way to sustainable cities and society is the massive growth of electric vehicles and producers of renewable energy (RE) like sun and wind. But these advances are a challenge in the more efficient management of energy due to the uncertainty that they incorporate into the problem. In this paper, a Cellular Estimation Distribution algo...
Optimization algorithms are important in problems of pattern recognition and artificial intelligence, i.e., the image recognition, face recognition, data analysis, optical recognition, etc. Estimation distribution algorithms (EDAs) is kind of optimization algorithms based on substituting the crossover and mutation operators of the Genetic Algorithms...
There are many problems were the objects under study are described by mixed data (numerical and non numerical features) and similarity functions different from the exact matching are usually employed to compare them. Some algorithms for mining frequent patterns allow the use of Boolean similarity functions different from exact matching. However, th...
In this paper, a new Cellular Estimation Bayesian Algorithm for discrete optimization problems is presented. This class of stochastic optimization algorithm with learning from the structure and parameters of local populations are based on independence test and decentralized populations scheme, which can reduce the number of function evaluations sol...
Frequent pattern mining is considered a key task to discover useful information. Despite the quality of solutions given by frequent pattern mining algorithms, most of them face the challenge of how to reduce the number of frequent patterns without information loss. Frequent itemset mining addresses this problem by discovering a reduced set of frequ...
Tariff design is one of the fundamental building blocks behind distributed energy grids. Designing tariffs involve considering customer preferences, supply and demand volumes and other competing tariffs. This paper proposes a broker capable of understanding the market supply and demand constraints to issue time-independent tariffs that can be offer...
The current energy scenario requires actions towards the reduction of energy consumptions and the use of renewable resources. To this end, the energy grid is evolving towards a distributed architecture called Smart Grid (SG). Moreover, new communication paradigms, such as the Internet of Things (IoT), are being applied to the SG providing advanced...
The clustering of Load Patterns (LPs) of customers has a broad range of applications, such as tariff formulation, power system planning, load forecasting, and so on. In this paper, we develop a multi-objective version of Differential Evolution (DE) algorithm using a Pareto Tournament (PT) selection to solve the LP clustering problem. Our automatic...
![Table 2 . θ parameter fine tuning in the range [0.9, 1] with 0.01 steps](profile/Ansel-Rodriguez-Gonzalez-2/publication/298819000/figure/tbl1/AS:613955496058896@1523389805434/th-parameter-fine-tuning-in-the-range-09-1-with-001-steps_Q320.jpg)
Electrical Load Pattern Shape (LPS) clustering of customers is an important part of the tariff formulation process. Nevertheless, the patterns describing the energy consumption of a customer have some characteristics (e.g., a high number of features corresponding to time series reflecting the measurements of a typical day) that make their analysis...
![Fig. 1. Major elements of the Power TAC scenario [8].](profile/Ansel-Rodriguez-Gonzalez-2/publication/281642727/figure/fig1/AS:341197538250752@1458359240129/Major-elements-of-the-Power-TAC-scenario-8_Q320.jpg)
Energy demand is increasing ubiquitously, and as cities grow larger they become more industrialized and more connected. Therefore new energy solutions that take advantage of today’s smart cities capabilities (like intelligent infrastructure, sensors, actuators and citizen engagement) should be deployed. This work studies some aspects of the energy...
Tariff design is one of the fundamental building blocks behind electricity markets. Designing tariffs involve considering customer preferences, supply and demand volumes and other competing tariffs. This paper proposes, and tests using Power TAC, a broker capable of understanding the market supply and demand constraints and issue time-independent t...
Obtaining the genome of an organism is a complex process which is possible to perform
merging DNA sequence fragments using overlaps between pairs of sequences. Currently,
existing technologies allow us to obtain DNA fragments with a length of hundreds of base
pairs. The process of dividing the genome into fragments is called DNA sequencing. There
a...
The output of some modern genome sequencing techniques consists
of short length DNA fragments known as reads. A disadvantage of short length
reads is that they may appear at different positions of the original genome sequence.
Not recognizing the position of repetitive fragments may generate gaps
in the final assembled sequence. This happens becaus...
Most of the current algorithms for mining association rules assume that two object subdescriptions are similar when they are exactly equal, but in many real world problems some other similarity functions are used. Commonly these algorithms are divided in two steps: Frequent pattern mining and generation of interesting association rules from frequen...
Most of the current algorithms for mining frequent patterns assume that two object subdescriptions are similar if they are
equal, but in many real-world problems some other ways to evaluate the similarity are used. Recently, three algorithms (ObjectMiner, STreeDC-Miner and STreeNDC-Miner) for mining frequent patterns allowing similarity functions d...
Text information processing depends critically on the proper representation of documents. Traditional models, like the vector space model, have significant limitations because they do not consider semantic relations amongst terms. Global Association Distance Model (GADM) is an alternative that includes this consideration for document representation...
This document is an extended abstract of the PhD thesis. The document present, an brief introduction to the problem. Also, the research question, the research aim, and the specific objectives are included. Finally , the main contributions of this research are enumerated and described.
In this paper, we focus on frequent pattern mining using non Boolean similarity functions. Several properties and propositions
that allow pruning the search space of frequent similar patterns, are proposed. Based on these properties, an algorithm for
mining frequent similar patterns using non Boolean similarity functions is also introduced. We eval...
La Minería de Patrones Frecuentes es una importante tarea dentro de la Minería de Datos. Es una realidad que en muchos conjuntos de datos la cantidad de patrones frecuentes es alta y por consiguiente son difíciles de analizar. Si además se utilizan funciones de semejanza distintas de la igualdad en el conteo de ocurrencias, esta cantidades aún más...
La mayoría de los algoritmos existentes para el minado de reglas de asociación asumen que dos subdescripciones de objetos son similares si y solo si ellas son iguales, sin embargo en problemas reales son usadas otras medidas de semejanzas. Esta propuesta de tesis doctoral aborda el problema de minería de reglas de asociación usando funciones de sem...
Frequent Pattern Mining is an important task due to the relevance of repetitions on data, also it is a fundamental step in the Association Rule Mining. Most of the current algorithms for mining frequent patterns assume that two object subdescriptions are similar if and only if they are equal, but in soft sciences some other similarity functions are...
Text information processing depends critically on the proper document representation. Traditional models, like vector space
model, have significant limitations because they do not consider semantic relations amongst terms. In this paper we analyze
a document representation that uses an association graph scheme model called Global Association Dista...
Text information processing depends critically on the proper representation of documents. Traditional models, like the vector space model, have significant limitations because they do not consider semantic relations amongst terms. In this paper we analyze a document representation using the association graph scheme and present a new approach called...
Document Retrieval process depends on the well document ranking and it lays on a proper documents representation. Traditional models, like the Vector Space Model, have significant limitations because they do not consider semantic relations amongst terms. In this paper we analyze document retrieval using the association graph scheme and present this...
En esta tesis se extienden los algoritmos con estimación de la distribución basados en redes simplemente conectadas (UMDA, PADAt2, PADAt3, PADAp2 y PADAp3) del caso binario al caso entero, se exploran sus comportamientos ante funciones con y sin interacciones no lineales entre las variables y se demuestra que el uso de la entropía al igual que en e...
Projects
Projects (10)
Goal: The new technological paradigms and the massive quantities of data available for analysis have introduced important challenges to research at all levels. To address such obstacles, many advances in the computational sciences have been employed to automate and improve the precision and efficiency of standard research methods. This special issue is interested in providing a comprehensive overview of the state-of-the-art techniques to model, optimize, quantify, or support decisions in the following fields Computational Intelligence Areas and social science disciplines (included but not limited):
Computational Intelligence Areas:
Artificial Neural Networks
Data Mining
Data Science
Deep Learning
Evolutionary Computation
Fuzzy reasoning
Machine Learning
Natural Language Processing techniques
Probabilistic Methods
Time Series Forecasting
Social Science Disciplines:
Anthropology
Communication studies
Economy
Education
Geography
Management
Political sciences
Psychology
Sociology
Tourism
Deadline for manuscript submissions: 01 September 2023
https://www.techscience.com/iasc/special_detail/social-scienceresearch
The Special Issue on “Machine Learning and Big Data Processing in Medical Decision Making" welcomes submissions exploring cutting-edge research and advances in this field. Potential topics include, but are not limited to, the following:
- Big Data (BD) and Data Science (DS) in health;
- Artificial Intelligence (AI) and machine learning (ML) in health;
- Semantic data-driven (SDD) solutions for health Informatics;
- SDD solutions for Internet of Things (IoT) in health;
- Knowledge discovery (KD), representation (KR), and exploitation (KE) applied in health;
- AI, ML, DS, and SSD solutions for medical physics.
Deadline for manuscript submissions: 20 May 2023
https://www.mdpi.com/journal/applsci/special_issues/0Q0NIZ93Z7
The theoretical framework of Reinforcement Learning will be extended, incorporating similarity and risk information to accelerate the convergence of learning and manage the risk of reaching catastrophic states. Three new RL algorithms will be developed based on the extension of the RL theoretical framework and three models for a complex case study: energy markets.