Alberto Ochoa-Zezzatti

Shipwrecked on Fear: Selection of Electives in School Minorities in a University Using Cuckoo Search Algorithm

The purpose of this research is to understand from a Multivariable optimization related with four scholar minorities studies in a University with approximately 87 educational studies on Bachelor level, this sample is composed by: (Safety Sciences, Interior Design, Sports Training and Aeronautics) assuming that any student want to analyze the way in which these minority groups selected electives to complete the set of credits in their respective studies to determine the optimal selection which involve the choice of these materials in educational majority groups to determine the benefit-cost associated with the term professional studies, whose main base the restriction on a small number of subjects in their studies this because such a low minority enrollment, even though this problem has been studied repeatedly by many researchers on the literature have not been established optimal values by supporting bio-inspired algorithms to interact with the different values associated with the achievement of the term loans and the cost-benefit every student to a minority group and comparing their choices of electives with respect the group. There are several factors that can influence the selection of an elective, for our research we propose to use a new bio-inspired algorithm called “Cuckoo search algorithm,” which has proven effective for the cohesion of behavior associated with several problems, and when and use restrictions have strategies to keep tempo in the selection of these materials, in our case, a resource such as time gain regarding the subjects studied is represented as the optimal way for the duration of the professional studies with uncertainty not know how long it can last set appropriate conditions for the selection of specialized subjects.

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.

Comparison of competitive learning for SOM used in classification of partial discharge

This paper shows different competitive learning algorithms for Self Organizing Map (SOM) and are experimentally compared, the characterization of the obtainable results in terms of quality of SOM. The competitive learning algorithms showed to SOM algorithm are Winner-takes-all, Frequency Sensitive Competitive Learning and Rival Penalized Competitive Learning. As a case study: the performance in classification of partial discharge on power cables.

Viral Analysis on Virtual Communities: A Comparative of Tweet Measurement Systems

This study shows the results of a comparison of different measurement systems that help measure tweets virality within virtual communities. Likewise, the history of this type of virtual social networks in the context of marketing are essential to creating effective proposals for the study of computer systems, software developers and marketing professionals and advertising are presented. Ultimately, a proposal for a graphic tweets measurement system is presented.

A comparative study of partial discharge by classification's kind

This paper presents a comparison of 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.

Diagnosis of partial discharge using self organizing maps and hierarchical clustering: an approach

This paper shows a first approach in a diagnosis selecting the different features to classify measured of partial discharges (PD) activities into underlaying insulation defects or source that generate PD. The results present different patterns using a hibrid method with Self Organizing Maps (SOM) and Hierarchical clustering, this combination constitutes an excellent tool for exploration analysis of massive data like partial discharge on underground power cables. The SOM has been used for nonlinear feature extraction. Therefore, the clustering method has been fast, robust, and visually efficient.

Handbook of Research on Military, Aeronautical, and Maritime Logistics and Operations

The Handbook of Research on Military, Aeronautical, and Maritime Logistics and Operations compiles interdisciplinary research on diverse issues related to logistics from an inclusive range of methodological perspectives. This publication focuses on original contributions in the form of theoretical, experimental research, and case studies on logistics strategies and operations management with an emphasis on military, aeronautical, and maritime environments. Readers:This chapter proposes to apply combinatorial optimization to past military conflicts with the aim of producing quantitative data that help explaining history. To do this, we can go beyond the classical “problem solving” vision of operations research that focuses on algorithmic development and computation analysis to privilege solution analysis and the needs of matching the obtained solution to the reality we aim to represent, study and analyze. In particular, we propose an iterative logic search method that aims to identify and analyze military strategic logic in terms of logistics. Then, to illustrate it, an application to the French troop assignment plan (Plan XVII of Joffre, 1932) is made to analyze which could be the subjacent logic behind the defense plan of French troops and state on the consequences of the optimization choices in terms of regional distribution of troops. A discussion of the proposed framework and the directions to generalize it will be presented as a conclusion.

Small Hydroponics Garden Improved Using Cultural Algorithms

The paper discusses a research related with the innovative sense of using Decision Support System based on a Bioinspired Algorithm related with an Agribussiness and hobby too about Hydroponics, to determine the correct and adequate selection of seeds in small spaces to build scenarios of location in the future to analyze the way to improve Mexican families’ economy, this research which permits select a specific number of seeds to cultivate each one of 25 different seeds –In a time horizon of a seasonal cultivate (approximately three months)-, these seeds are evaluated from a information repository with data from another suceesful hydroponics systems. Each harvest was analyzed to built their cost-benefit during different times and scenarios and determine the viability of cultivate in the time horizon using a formal methodology based on Bioinspired Algorithms. The group of 25 seeds cultivated by “Leguizamo Povedano Cooperative” is characterized and analyzed by obtain the most representative and sucessful future scenario to determine the quantity of seeds cultivated which try to improve the limited resources and the perspectives of determine the correct selection to stablishment a signifcately life day. A case of study is presented regarding to the proposal horizons using data obtained from the Repository of the Cooperative. The intention of the present research is to apply the computational properties; in this case of established a Model of Hydropoics cultivate in a Cooperative. In addition, we analyzed the selection and location of location to cultivate a specific seed using a similarity model to locate this. The sample of study allowed analyzing the individual features of each harvest with the emulation from set matching features (commercialization, climate, in others). By means of this is possible to predict the best location to cultivte.

Improve Decision Support Using Adaptive Data Mining

Nowadays is necessary to take decisions based in the knowledge obtained through advanced techniques of date analysis, decision tree is an interesting option. In this work a Rich Internet Application to visualize a decision tree in a mobile device is presented. This application lets deploy the complete tree decision and the categorization of new registers, with this tool is possible to take decisions based in the analysis of data in an extended data base. The application is developed with the framework “ZK” and requires a mobile device with internet connection capability and a web browser that support this kind of applications like: “Opera Mobile” or “Safari Mobile”.