• Approaches based on tree-structures classifiers to protein fold prediction

      Mauricio-Sanchez, David; de Andrade Lopes, Alneu; higuihara Juarez Pedro Nelson (Institute of Electrical and Electronics Engineers Inc., 2017-08)
      Protein fold recognition is an important task in the biological area. Different machine learning methods such as multiclass classifiers, one-vs-all and ensemble nested dichotomies were applied to this task and, in most of the cases, multiclass approaches were used. In this paper, we compare classifiers organized in tree structures to classify folds. We used a benchmark dataset containing 125 features to predict folds, comparing different supervised methods and achieving 54% of accuracy. An approach related to tree-structure of classifiers obtained better results in comparison with a hierarchical approach.
    • Information architecture model for the successful data governance initiative in the peruvian higher education sector

      Castillo, Luis Felipe; Raymundo, Carlos; Mateos, Francisco Dominguez (Institute of Electrical and Electronics Engineers Inc., 2017-08)
      The research revealed the need to design an information architecture model for Data Governance initiative that can serve as an intercom between current IT / IS management trends: Information technology (IT) management and information management. A model is needed that strikes a balance between the need to invest in technology and the ability to manage the information that originates from the use of those technologies, as well as to measure with greater precision the generation of IT value through the use of quality information and user satisfaction, using the technologies that make it possible for the information to reach them to be used in their daily work.
    • Predictive modeling for presumptive diagnosis of type 2 diabetes mellitus based on symptomatic analysis

      Barrios, Ordonez; Alberto, Diego; Infantes, Vizcarra; Raphael, Erick; Aguirre, Armas; Alexander, Jimmy (Institute of Electrical and Electronics Engineers Inc., 2017-08)
      The purpose of using Predictive Modeling for presumptive diagnosis of Type 2 Diabetes Mellitus based on symptomatic analysis is the optimization of the diagnosis phase of the disease through the process of evaluating symptomatic characteristics and daily habits, allowing the forecasting of T2DM without the need of medical exams through predictive analysis. The tool used was SAP Predictive Analytics and in order to identify the most suitable algorithm for the prediction, we evaluated them based on precision and false positive/negative relations, having found the Auto Classification algorithm as the most accurate with a 91.7% precision and a better correlation between false positives (8) and false negatives (3).
    • Software defined radio for hands-on communication theory

      Miyashiro, Hector; Medrano, Melissa; Huarcaya, Jose; Lezama, Jinmi (Institute of Electrical and Electronics Engineers Inc., 2017-08)
      Based on a workshop developed at INICTEL-UNI, this paper presents the methodology and considerations taken to improve the experiences in communication laboratory sessions in Peruvian universities with Software Defined Radio platforms, using a HackRF-One for transmission and a RTL-SDR for reception together with GNUradio Companion, an open source software. The use of these tools allows real communications to be implemented with low cost, simplicity and flexibility that represents a perfect combination for undergraduate laboratory sessions. In addition, it is also presented an 8-PSK communication system implementation as an experience to digital communications as a viability of this proposal.