Machine Learning algorithms are very sensitive to missing data, and their results depend on the way data is treated by the algorithms. In this work, we will understand missing data when a variable in a dataset is missing data but cannot be eliminated due to its relevance, or when some records of the dataset are missing the values of some of the variables. In general, these problems can be addressed during data preprocessing, for example, by removing variables with a lot of missing data. Now, our interest is to evaluate the robustness of the LAMDA (Learning Algorithm for Multivariable Data Analysis) algorithm to handle missing data at the variable level. A series of experiments will be carried out exposing the algorithm to cases with different percentages of missing variables, and the results obtained will be analyzed. In addition, the performance of the algorithm will be assessed compared to other machine learning algorithms in the context of classification and clustering problems. to carry out this study, several datasets are used. Multiple copies of each of them have been obtained, subjecting them to various transformations that allow us to study different case studies to test the effectiveness of the proposed algorithm.

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%.

A growing trend in information technology is not just reacting to changes, but anticipating them as much as possible. This paradigm is the base of modern applications, such as recommendation systems, the context-aware applications, among others. Anticipatory systems extend the idea to the communication systems, by studying patterns and periodicity in human behavior and network dynamic, to optimize the network performance. Particularly, for context-awareness applications is very important to extract autonomously contextual information. This work proposes a set of autonomic cycles of data analysis tasks to provide context awareness using the network traffic data, which gives information about the behavior of the traffic flow in a given context. This information about the network (links, users, type of traffic, etc.) is used to extract useful knowledge about the context using data analysis tasks.

An emerging serious game (SG) is a game designed and developed from an objective other than pure fun, so that the player can learn while playing, but where the game unfolds spontaneously, autonomously, and without explicit laws, adapting to the player. On the other hand, an emerging serious game engine (ESGE) must make explicit the possibility of emergence in an SG, from the coordinated handling of the game plot, adapted to the specific educational context where it is being used. In previous articles, an ESGE has been proposed, which allows the initial emergence of an SG. This article defines a new component of the ESGE, called the plot adaptive system (PAS), which allows the updating of an SG according to the theme that is being given in a smart classroom (SaCI). This component is based on an ant colony optimization algorithm that changes the plots/frames in the game to follow the desired theme in the SaCI. Thus, this new component manages a set of game plots that may be of interest in an educational context to dynamically modify an SG initially conceived for a subject taught in the SaCI, in order to adequate it to the current pedagogical context. The novelty of the approach is that it adapts the SG to the educational context where it takes place. Additionally, this article analyzes the behavior of the PAS in a case study, and presents three experiments with very encouraging results.

A serious emerging games engine (SEGE) must make explicit the possibility of emergence in a serious game (SG), from the coordinated handling of game plot, adapted to the specific educational context where it is being developed. In previous articles a SEGE based on the ant colony optimization algorithm (ACO) has been proposed, which allows the initial emergence of a SG. In the present work the adaptive plot system (APS) is specified, which allows the emergence of plot in a serious game emerging (SGE), and is based on an ACO that changes the plot in the game, according to the theme that is being given in a smart classroom SaCI (Salón de Clase Inteligente, for its acronym in Spanish),). The APS performs the management of a set of game plot that may be of interest in a context-educational domain, in such a way to adapt the SGE initially conceived to the subject taught in the SaCI, in order to make the appropriate SGE emerge Pedagogical process in progress. Additionally, this paper analyzes the behavior of APS in a case study, showing very encouraging results as SEGE.