Alberto Ochoa

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.

Implementing Data Mining to Improve a Game Board Based on Cultural Algorithms

Abstract —Evolutionary computation is a generic term used to make reference to the solution of computational problems planned and implemented based on models of an evolutionary process. Most of the evolutionary algorithms propose biological paradigms, and the concepts of natural selection, mutation and reproduction. However, other paradigms that can be adopted in the creation of evolutionary algorithms exist. Many problems involve not structured environments that can be considered from the perspective of cultural paradigms; the cultural paradigms offer a wide range of categorized models that ignore the possible solutions to the problem, -a commo n situation in the real life-. The purpose of the present work is to apply the computational properties of the cultural technology; on this case, to corroborate them by means of data mining to propose the solution to a specific problem. The above mentioned, carrying out an adaptation from the perspective of the societies modeling. An environment to carry out tests of this type was develope d to allow the learning on the not very conventional characteristics of a cultural technology. This environment is called Baharastar.

Algorithm Selection: From Meta-Learning to Hyper-Heuristics

In order for a company to be competitive, an indispensable requirement is the efficient management of its resources. As a result derives a lot of complex optimization problems that need to be solved with high-performance computing tools. In addition, due to the complexity of these problems, it is considered that the most promising approach is the solution with approximate algorithms; highlighting the heuristic optimizers. Within this category are the basic heuristics that are experience-based techniques and the metaheuristic algorithms that are inspired by natural or artificial optimization processes.

Musical Recommendation on Thematic Web Radio

In this research is described a Musical Recommender System associated to Thematic Web Radio for songs in a database related with this Web Radio employs the Dublin Core metadata standard for the documents description, the XML standard for describing user profile, which is based on the user’s profile, and on service and data providers to generate musical recommendations. The main contribution of the work is to provide a recommendation mechanism based on the user of this Thematic Web Radio reducing the human effort spent on the profile generation. In addition, this article presents and discusses some experiments that are based on quantitative and qualitative evaluations.

Social Data Mining to Improve Bioinspired Intelligent Systems

The proposal of this chapter is to explain the implementation of social data mining to improve results in bioinspired intelligent systems using generation of clusters, associative rules; decision trees, associated models, dioramas and multivariable analysis for obtain knowledge about any issue related with a topic. This kind of intelligent systems using bioinspired computing – specially, group intelligence techniques such as: Ant Colony, Particle Swarm Optimization and Cultural Algorithmsthat try to simulate biological processes that occur in the nature. Intelligent agents use this information to make decisions to improve a needed heuristic optimization in different fields such as: negotiation, argumentation or artificial societies simulation. First in section 2 of this chapter, we approach different concepts related with social data mining and how to use different ways to analyze and model the necessary information to support the correct decision of agents; in next three sections we explain the way to generate a specific behaviour by using group intelligence techniques –ant colony (section 3), particle swarm optimization (section 4) and cultural algorithms (section 5), In section 6, we apply this knowledge in diverse fields and application domains that use a heuristic optimization. In section 7 we compare different cases of studies: Eurovision Voting problem, and the Distribution of Elements. Finally in section 8 we provide our conclusions and outline our future research.

Using multi-objective optimization to design parameters in electro-discharge machining by wire

The following paper describes the main objective to follow the methodology used and proposed to obtain the optimal values of WEDM process operation on the machine Robofil 310 by robust parameter design (RPD) of Dr. G. Taguichi [TAGUCHI, G. 1993], through controllable factors which result in more inferences regarding the problem to noise signal (S / N), which for this study is the variability of the hardness of samples from 6061, also studied the behaviour of the output parameters as the material removal rate (MRR) and surface roughness (Ra), subsequently took the RPD orthogonal array and characterized the individuals in the population, each optimal value is a gene and each possible solution is a chromosome, used multi-objective optimization using Non-dominated Sorting Genetic Algorithm to cross and mutate this population to generate better results MRR and Ra.

Improved solution based on Bat Algorithm to Vehicle Routing Problem in a Caravan Range Community

The purpose of this research is to understand from a Multivariable optimization associated with the path of a group of vehicles integrated in a Community Caravan Range and determine the optimal route involve speed, storage and travel resources for determining the cost benefit have partnered with a travel plan, which has as principal basis the orography restriction, although this problem has been studied on several occasions by the literature failed to establish by supporting ubiquitous computing for interacting with the various values associated with the achievement of the group of vehicles and their cost-benefit of each member of the community and comparing their individual trips for the group. There are several factors that can influence in the achievement of a group trip, for our research we propose to use Algorithm Bat on, which has proven to be efficient for the convergence of several issues (artificial bats) when they have such restrictions and obstacles should use this energy to avoid in our case, a gain resource as food which in our case is represented as the use of food optimally for the duration of a long trip with the uncertainty of not knowing when you have a resupply.