Email: karina.ponce@sdas-group.com

She has born in Otavalo – Ecuador in 1993. He received his degree in electronic and networking engineering from the Universidad Técnica del Norte, Ibarra – Ecuador in 2017. He worked as an assistant teacher at the same university from 2017 to 2018. Nowadays, she is a Master's student in Computer Science at Universidade Federal de Pernambuco, Recife – Brazil. Her research interests are Big Data, data mining, clustering, neural networks, embebed systems, and networking.

Research Interests

Big Data, data mining, clustering, neural networks, embebed systems, networking

Publications

1. Developments on Solutions of the Normalized-Cut-Clustering Problem Without Eigenvectors
   Lecture Notes in Computer Science
   Leandro Leonardo Lorente-LeyvaIs, rael David Herrera-Granda, Paul D. Rosero-Montalvo, Karina L. Ponce-Guevara, Andrés Eduardo Castro-Ospina, Miguel A. Becerra, Diego Hernán Peluffo-Ordóñez
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   Abstract

   Normalized-cut clustering (NCC) is a benchmark graph-based approach for unsupervised data analysis. Since its traditional formulation is a quadratic form subject to orthogonality conditions, it is often solved within an eigenvector-based framework. Nonetheless, in some cases the calculation of eigenvectors is prohibitive or unfeasible due to the involved computational cost – for instance, when dealing with high dimensional data. In this work, we present an overview of recent developments on approaches to solve the NCC problem with no requiring the calculation of eigenvectors. Particularly, heuristic-search and quadratic-formulation-based approaches are studied. Such approaches are elegantly deduced and explained, as well as simple ways to implement them are provided.

2. GreenFarm-DM: A tool for analyzing vegetable crops data from a greenhouse using data mining techniques (First trial)
   Second Ecuador Technical Chapters Meeting (ETCM)
   K. L. Ponce-Guevara, J. A. Palacios-Echeverría, E. Maya-Olalla, H. M. Domínguez-Limaico, L. E. Suárez-Zambrano, P. D. Rosero-Montalvo, D. H. Peluffo-Ordóñez, J. C. Alvarado-Pérez
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   Abstract

   This work shows the use of Big Data and Data Mining techniques on vegetable crops data from a greenhouse by implementing the first version of a software tool, so called GreenFarm-DM. Such a tool is aimed at analyzing the factors that influence the growth of the crops, and determine a predictive model of soil moisture. Within a greenhouse, the variables that affect crop growth are: relative humidity, soil moisture, ambient temperature, and levels of illumination and CO2. These parameters are essential for photosynthesis, i.e. during processes where plants acquire the most nutrients, and therefore, if performing a good control on these parameters, plants might grow healthier and produce better fruits. The process of analysis of such factors in a data mining context requires designing an analysis system and establishing an objective variable to be predicted by the system. In this case, in order to optimize water resource expenditure, soil moisture has been chosen as the target variable. The proposed analysis system is developed in a user interface implemented in Java and NetBeans IDE 8.2, and consists mainly of two stages. One of them is the classification through algorithm C4.5 (chosen for the first trial), which uses a decision tree based on the data entropy, and allows to visualize the results graphically. The second main stage is the prediction, in which, from the classification results obtained in the previous stage, the target variable is predicted from information of a new set of data. In other words, the interface builds a predictive model to determine the behavior of soil moisture.