The problem of timetabling events is present in various organizations such as schools, hospitals, transportation centers. The purpose of timetabling activities at a university is to ensure that all students attend their required subjects in accordance with the available resources. The set of constraints that must be considered in the design of timetables involves students, teachers and infrastructure. This study shows that acceptable solutions are generated through the application of genetic, memetic and immune system algorithms for the problem of timetabling. The algorithms are applied to real instances of the University of Mumbai in India and their results are comparable with those of a human expert.

The process of classification of the raw material, is one of the most important procedures in any tea dryer, being responsible for ensuring a good quality of the final product. Currently, this process in most tea processing companies is usually handled by an expert, who performs the work manually and at his own discretion, which has a number of associated drawbacks. In this work, a solution is proposed that includes the planting, design, development and testing of a prototype that is able to correctly classify photographs corresponding to samples of raw material arrived at a dryer, using intelligence techniques (IA) type supervised for Classification by Artificial Neural Networks and not supervised with K-means Grouping for class preparation. The prototype performed well and is a reliable tool for classifying the raw material slammed into tea dryers.

This study describes a model of explanations in natural language for classification decision trees. The explanations include global aspects of the classifier and local aspects of the classification of a particular instance. The proposal is implemented in the ExpliClas open source Web service [1], which in its current version operates on trees built with Weka and data sets with numerical attributes. The feasibility of the proposal is illustrated with two example cases, where the detailed explanation of the respective classification trees is shown.

Technological advances have allowed to collect and store large volumes of data over the years. Besides, it is significant that today's applications have high performance and can analyze these large datasets effectively. Today, it remains a challenge for data mining to make its algorithms and applications equally efficient in the need of increasing data size and dimensionality [1]. To achieve this goal, many applications rely on parallelism, because it is an area that allows the reduction of cost depending on the execution time of the algorithms because it takes advantage of the characteristics of current computer architectures to run several processes concurrently [2]. This paper proposes a parallel version of the FuzzyPred algorithm based on the amount of data that can be processed within each of the processing threads, synchronously and independently.

This paper proposes an innovative way to address real cases of production prediction. This approach consists in the decomposition of original time series into time sub-series according to a group of factors in order to generate a predictive model from the partial predictive models of the sub-series. The adjustment of the models is carried out by means of a set of statistic techniques and Automatic Learning. This method was compared to an intuitive method consisting of a direct prediction of time series. The results show that this approach achieves better predictive performance than the direct way, so applying a decomposition method is more appropriate for this problem than non-decomposition.