The Optimal combination: Grammatical Swarm, Particle Swarm Optimization and Neural Networks.

Social behaviour is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks.

Usability study for the design of a communicator for ICU patients with communication difficulties

Most of the patients that reside in the intensive care unit experience fear, frustration and high levels of anxiety as they are not able to communicate properly. In this sense, the use of communication tools can be helpful to reduce the frustration levels and also, to improve the efficiency and the speed of the communication. The objective of this work, is to design a tool that allows solving the communication problems that patients suffer when they are admitted in the intensive care unit. In order to achieve the objective of this work, a qualitative study that involved interviews with former patients, hospital staff members and family relatives was performed. Afterwards, the design of a prototype was developed to later conduct and analyze usability evaluations with former patients, hospital staff members and patients relatives. The results expose that participants of the usability evaluations were able to perform most of the tasks effectively.