Università degli Studi eCampus

About: Università degli Studi eCampus is a education organization based out in Novedrate, Italy. It is known for research contribution in the topics: Anxiety & Planck. The organization has 124 authors who have published 538 publications receiving 21483 citations. The organization is also known as: Universita degli Studi eCampus.

A Distributed Fuzzy Associative Classifier for Big Data

Abstract: Fuzzy associative classification has not been widely analyzed in the literature, although associative classifiers (ACs) have proved to be very effective in different real domain applications. The main reason is that learning fuzzy ACs is a very heavy task, especially when dealing with large datasets. To overcome this drawback, in this paper, we propose an efficient distributed fuzzy associative classification approach based on the MapReduce paradigm. The approach exploits a novel distributed discretizer based on fuzzy entropy for efficiently generating fuzzy partitions of the attributes. Then, a set of candidate fuzzy association rules is generated by employing a distributed fuzzy extension of the well-known FP-Growth algorithm. Finally, this set is pruned by using three purposely adapted types of pruning. We implemented our approach on the popular Hadoop framework. Hadoop allows distributing storage and processing of very large data sets on computer clusters built from commodity hardware. We have performed an extensive experimentation and a detailed analysis of the results using six very large datasets with up to 11 000 000 instances. We have also experimented different types of reasoning methods. Focusing on accuracy, model complexity, computation time, and scalability, we compare the results achieved by our approach with those obtained by two distributed nonfuzzy ACs recently proposed in the literature. We highlight that, although the accuracies result to be comparable, the complexity, evaluated in terms of number of rules, of the classifiers generated by the fuzzy distributed approach is lower than the one of the nonfuzzy classifiers.

A Comparison between Position-Based and Image-Based Dynamic Visual Servoings in the Control of a Translating Parallel Manipulator

Abstract: Two different visual servoing controls have been developed to govern a translating parallel manipulator with an eye-in-hand configuration, That is, a position-based and an image-based controller. The robot must be able to reach and grasp a target randomly positioned in the workspace; the control must be adaptive to compensate motions of the target in the 3D space. The trajectory planning strategy ensures the continuity of the velocity vector for both PBVS and IBVS controls, whereas a replanning event is needed. A comparison between the two approaches is given in terms of accuracy, fastness, and stability in relation to the robot peculiar characteristics.