A typical problem that is solved by supervised machine learning techniques is classification. There are many types of this problem, such as binary classification, multi-classification, and multi-labels. The development of techniques that perform well in different types of classification problems, and for datasets with many or few variables, or many or few data, remains a challenge. On the other hand, recent work indicates that machine learning can benefit from quantum computing in terms of reducing computational complexity and improving its performance. The purpose of this research is to analyze the use of Quantum Support Vector Machines (QSVM) for different types of classification problems. For this, several kernels (linear and Gaussian) are considered. In turn, a comparison is made with Random Forest. Also, a detailed analysis is carried out for the binary classification of coffee beans, obtaining a precision of 95% and an accuracy of 79%.

The concept of digital transformation involves exploiting digital technologies to generate new ways of doing things in organizations, including the creation of new processes, models, and services that produce value based on the digitization of data and processes. The application of digital technologies enables organizations to develop capabilities for innovation, automation, etc., utilizing both established and emerging technologies, including the widespread use of artificial intelligence. In this paper is proposed the implementation of a fuzzy cognitive map based on experts for the evaluation of the level of digital transformation in MSMEs (Micro, Small & Medium Enterprises). The main variables of digital transformation used to define our fuzzy cognitive map were classified into five types: i) Organization and Culture variables related to strategies, way of working, and ecosystems, ii) Customer variables related to services and digital channels and products, iii) Operations and Internal Processes variables related to supply chain, suppliers, business model, iv) Information Technologies variables related to innovation, digitization, data and analytic. Finally, the fifth type of variable is the target, which indicates the level of digital transformation of the organization. Our model managed to specify with 99.4% the level of digital transformation of the organization. Thus, our fuzzy cognitive map can predict and analyze the factors associated with digital transformation in MSMEs.

This article specific a management system of the learning cloud, based on the ODA (Ontology Driven Architecture) and SOA (Service Oriented Architecture) paradigms, of the educational online platform defined for the Career in Computer Science and Informatics in the University of the Andes in Mérida. This platform consists of three clouds: Formation Cloud, composed of the graph in which is defined the curricular profiles that a student can take to get a degree/diploma; Knowledge Sources Cloud, where learning objects and online courses appear as sources of knowledge to meet the learning units defined in curricular graph; and finally, the Learning Cloud, where the paradigms, strategies and learning tools appear, so she brings learning mechanisms necessary for the formation of the student.