Julio Ponce

Searching research papers using clustering and text mining

The time spent by users are almost two or more hours looking for papers that generates the possibility to make a search engine to optimize and precision in the results. This works purposes a better classification of research papers, the architecture works with a database of knowledge related with the topics of programming, databases and operating systems. Thats the initial work of a classification using text mining techniques to search into the documents with natural language contained and get the best words of their content to get a database knowledge, thats the first step to get the desired knowledge also the proposed work use the same engine to make searches classifying the information introduced by the final user and searching in the correct cluster.

Analysis of Cyber-bullying in a virtual social networking

This paper focuses on the social and cultural implications of cyber technologies. Identity, bullying and inappropriate use of communication are major issues that need to be addressed in relation to communication technologies for the security in the Web use. The contribution of this paper is to present a novel approach to explain the performance of a novel Cyber-bullying model applied on a Social Network using Multiagents to improve the understanding of this social behavior.

Artificial Societies and Social Simulation using Ant Colony, Particle Swarm Optimization and Cultural Algorithms

The proposal of this chapter is to explain the implementation of collective intelligent techniques to improve results in artificial societies and social simulation using diverse concepts such as argumentation, negotiation and reputation models to improve social simulation of artificial societies implementing dioramas, and multivariable analysis in different application domains for example Logistics. These techniques will be useful for answering diverse queries after gathering general information about a given topic. This kind of collective intelligence will be characterized by: ant colony, particle swarm optimization, and cultural algorithms, each one of these implementing diverse models or agents for simulate a social behaviour. Intelligent agents are used to obtain information to take decisions that try to improve the heuristic optimization needed in different application and fields of knowledge. First, in section 1 of this paper, we approach different concepts related with Artificial Societies and Social Simulation using different strategies to analyze and model the necessary information to support the correct decisions of the evolving models. In other sections we explain the way to generate a specific behaviour with collective-intelligence techniques –ant colony (section 2), particle swarm optimization (section 3), and cultural algorithms (section 4). In section 5 we apply this knowledge in diverse fields and application domains that needs a heuristic optimization and the more innovative perspectives of each technique. In

Bat algorithm to improve a Financial Trust Forest

A controversial topic and frequently in public policy analysis is related with temporality in projects with limited funds associated with natural resources. Public resources can be organized as a Financial Trust. Very often the relationship between the budgets requested and can be received is overwhelming, as it is very unlikely to be as necessary as that can be awarded. In addition, strategic approaches, political and ecological considerations permeate the decision-making on such assignments. To meet these regulatory criteria, underlying any prevailing public policy or government ideology, it is clear that both must be appropriate to prioritize the development projects in ecological project portfolios, these must be consistent with principles sound (for example, maximization of social benefits in the future). Computation so using novel bioinspired algorithms (In this case a Bat Algorithm) can be characterized as follows: · They can be no doubt profitable, but its benefits are indirect, perhaps only in the long run may be visible and difficult to quantify. · Apart from its potential contribution to economic welfare, are not intangible benefits in the present, which must be considered to achieve a holistic view of their ecological and social impact. · Equity in relation to the magnitude of the impact of a specific project and social conditions of the beneficiaries should also be considered. In the present study was conducted using an approach to intelligent optimization problem for a Financial Trust Forest in Chihuahua.

Data Mining in Web Applications

The World Wide Web is rapidly emerging as an important medium for commerce as well as for the dissemination of information related to a wide range of topics (e.g., business and government). According to most predictions, the majority of human information will be available on the Web. These huge amounts of data raise a grand challenge, namely, how to turn the Web into a more useful information utility (Garofalakis et al., 1999) . At the moment with the popularity of Internet, people are exhibited to a lot of information that is available for study. Nowadays there is also a great amount of applications and services that are available through Internet as they are seeking, chats, sales, etc., nevertheless much of that information is not useful for many people, but in the area of Data Mining, all the information available in the Internet represents a work opportunity and it is possible to do a lot of analysis on the basis of these with specific purposes. Knowledge Discovery and Data Mining are powerful data analysis tools. The rapid dissemination of these technologies calls for an urgent examination of their social impact. We show an overview of these technologies. The terms “Knowledge Discovery” and “Data Mining” are used to describe the ‘non-trivial extraction of implicit, previously unknown and potentially useful information from data (Wahlstrom & Roddick, 2000). Knowledge discovery is a concept that describes the process of searching on large volumes of data for patterns that can be considered knowledge about the data. The most well-known branch of knowledge discovery is data mining.

Evolvable Metaheuristics on Circuit Design

Evolutionary computation algorithms are stochastic optimization methods; they are conveniently presented using the metaphor of natural evolution: a randomly initialized population of individuals evolves following a simulation of the Darwinian principle. New individuals are generated using genetic operations such as mutation and crossover. The probability of survival of the newly generated solutions depends on their fitness (Michalewicz et al., 1995). Evolutionary algorithms (EAs) have been successfully used to solve different types of optimization problems (Back, 1996). In the most general terms, evolution can be described as a two-step iterative process, consisting of random variation followed by selection. The structure of any evolutionary computation algorithm is shown in the figure 1.

Resolution of a Combinatorial Problem using Cultural Algorithms

Many problems involve not structured environments which can be solved from the perspective of Bioinspired Algorithms (Cultural Algorithms). In this paper, a proposed algorithm is used to resolve a famous game known as Japanese puzzles, which are analyzed for obtain the optimal solution. The authors show that Japanese Puzzles are constrained combinatorial optimization problems, which can be solved using Cultural Algorithms. Other features, such the use of a belief space involve many proposed solutions and local search heuristics; can also be taught using these puzzles.

Determining the Ranking of a New Participant in Eurovision Using Cultural Algorithms and Data Mining

Evolutionary computation is generic name given to the resolution of computational problems with base in models of an evolutionary process. Most of the evolutionary algorithms propose biological paradigms, and concepts of natural selection, mutation and reproduction. Nevertheless other paradigms exist which can be adopted in the creation of evolutionary algorithms. Many problems involve not structured environments which can be solved from the perspective of cultural paradigms and, which offer plenty of category models where one does not know the possible solutions of a problem, a common situation in real life. The intention of the present work is to apply the computational properties of cultural technology, in this case to corroborate by means of data mining and, to propose the solution of a specific problem, adapted from the literature about society modelling. In addition, we analyze the voting behavior and ratings of judges in a popular song contest held every year in Europe. The dataset makes it possible to analyze the determinants of success, and gives a rare opportunity to run a direct test of vote trading from logrolling. We show that they are rather driven by linguistic and cultural proximities between singers and voting countries. With this information it is possible to predict the score of a new country, redistributed the assigned votes for a lot of the participants, this paper tries to explain this social behaviour.

Competitive learning for Self Organizing Maps used in classification of partial discharge

This paper presents some competitive learning algorithms for Self Organizing Map (SOM). The competitive learning algorithms showed to self organizing map algorithm are winner-takes-all, Frequency Sensitive Competitive Learning and Rival Penalized Competitive Learning. The result shows the performance in classification of partial discharge on power cables using SOM.

Biometric Data Mining Applied to On-line Recognition Systems

Data mining has become an increasingly popular activity in all areas of research, from business to science, biometrics being no exception. Data mining is the computer-intensive activity of exploring large data sets with the purpose of discovering, within a subset of data, some relationship of patterns or hypothesis that may be worthy of further study (Hernandez-Aguilar et al., 2008; Amaratunga & Cabrera, 2004). According to a widely accepted definition, knowledge discovery in databases (KDD), more widely known as data mining, is a non-trivial process of identifying valid, novel, potentially useful and understandable patterns in data (Fayyad et al., 1996).