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LA-CCI is an acronym that refers to the Latin American Conference on Computational Intelligence (CI) that, in this year, is sponsored by the IEEE Colombia Section and IEEE Computational Intelligence Society. Currently, countries as Argentina, Bolivia, Brazil, Chile, Ecuador, Colombia, Mexico, Peru and Venezuela contribute to promote the CI, exchanging experiences/personnel and activities as present...
Human detection in digital videos is challenging since the human appearance may widely vary. Several algorithms to detect humans in digital images have been recently developed, such as the Aggregated Chanel Features (ACF). Most of them are based on features related to the shape. These algorithms give the best results regarding accuracy but generate many false alarms. In this paper, we propose to use...
From a mathematical model given by the integral Im,n=∫Pm(x)G(x)Jn(x)dx where Pm(x) a polynomial function, G(x) a Gaussian function, and Jn(x) is the integer-order Bessel function, we have performed simulations to evaluate the possible scenario of outbreak of Zika virus (ZIKV) in a big city. Our study is motivated from the outbreaks in Brazil (2007). We have assumed a list of probabilities which are...
Phonetic deviations (dyslalia) are frequent speech disorders that lead most patients to speech therapy clinics. Nowadays one can find software able to support the treatment of phonetic deviations with several different approaches. However, none of the most used alternatives are easily accessible, are low cost and/or are directed towards individualization for an individual patient care. In this research...
In this work, we define several theorems for a pattern-matching model based on the systematic functioning of the human neocortex. For the design model, we draw inspiration in the pattern recognition theory of mind, which describes a basic algorithm of the neocortex. The proposed model exploits the idea of recursivity in the recognition process, or unbundling/integration of pattern to recognize. The...
Assembly lines constitute the main production paradigm of the contemporary manufacture industry. Thus, many optimization problems have been studied aiming to improve the efficacy of its use. In this work, the Fish School Search algorithm and a variation of it that incorporates a routine to avoid stagnation of the search process were applied in order to solve the Simple Assembly Line Balancing Problem...
Open-shop scheduling problems are highly complex (NP-Hard), where solution is represented by a permutation of machines order that each job must follow to be processed, and for each job, it can have a different machines order. The main goal in these problems is to reduce consumption resources, and at same time, improving several performance criteria. In this work we propose a computational strategy...
In this work, a novel color quantization approach based on the Growing Neural Forest (GNF) is proposed. The GNF is a recently proposed improvement of the Growing Neural Gas (GNG), where a set of trees is learned instead of a general graph. Thus, this model is suitable for color quantization purposes. Experimental results confirm the good performance of the GNF for color quantization tasks.
There are many Evolutionary Algorithms which main features are: population, evolutionary operations (crossover, mate, mutation and others). Most of them are based on randomness and follow a criteria using fitness like selector. The proposal uses probability density function according to best of initial population to sample new population and save better individuals iteratively. Then using centroid...
Gray-Box Models which combine a phenomenological model with a black box tool are useful for determining the values of not well known parameters of the model. In this work an indirect strategy for training these gray box models using least-square support vector machine and genetic algorithms is presented. The gray box model was tested in a Continuous Stirred Tank Reactor process with good results (Index...
EEG recordings are often contaminated by environmental noise and physiological factors commonly known as artifacts, which affect the quality of the signal. This paper presents an automatic method to classify between artifactual and neural components in EEG signals using an Independent Component Analysis (ICA) and a Support Vector Machine. With the resultant model, we obtained a classification accuracy...
Thermal comfort conditions are important for the normal development of human tasks, and as such they have been analyzed in the context of several areas including human physiology, ergonomics, heating and cooling systems, architectural design, etc. In this work, we analyze the estimation of the thermal comfort perception by human subjects using a neurocomputational model based on the C-Mantec constructive...
This work presents the development of a navigation algorithm based on fuzzy logic for the displacement of a mobile robot. The goal is to develop an autonomous navigation for an assistance mobility device for elderly people. The robot, which is the base of the robotic device, must be able to autonomously navigate in an unstructured environment, guiding the user to a target, avoiding possible obstacles...
The choice of a good clustering algorithm is vital in many tasks to optimize results. Nowadays, the most used algorithms use only one strategy to find and form the clusters of data, which can limit the effectiveness of the process. This paper presents a new approximation to clustering, called Essence-Based Clustering, that combines multiple strategies in a series of steps, allowing two levels of configuration...
The problem of concurrent design of a mechanism can be defined as finding optimal structural parameters and control parameters for a given objective function during the same optimization process. In this paper, a general concurrent optimization methodology for kinematically complex mechanisms is tested using a Delta manipulator. This methodology intends to optimize any structure and control design,...
In this paper, it is proposed a neural network based on by AutoAssociative Pyramidal Neural Network and their architecture, which uses concepts of receptive fields and autoassociative memory. These concepts are widely used in models of artificial neural networks and were incorporated into model proposed in this work. Furthermore, the proposed neural network also uses the concept of sharing weights...
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