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Hugo Siqueira

Researcher at Federal University of Technology - Paraná

Publications -  78
Citations -  1094

Hugo Siqueira is an academic researcher from Federal University of Technology - Paraná. The author has contributed to research in topics: Particle swarm optimization & Computer science. The author has an hindex of 15, co-authored 70 publications receiving 559 citations. Previous affiliations of Hugo Siqueira include Universidade de Pernambuco & State University of Campinas.

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Assessing the impact of PM2.5 on respiratory disease using artificial neural networks

TL;DR: Nonlinear Artificial Neural Networks could be a more sensitive method than statistical regression models for assessing the effects of air pollution on respiratory health, and especially useful when there is limited data available.
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Swarm intelligence for clustering — A systematic review with new perspectives on data mining

TL;DR: A systematic mapping review on recent investigations of swarm-inspired algorithms to tackle clustering problems and provides an overview of how to apply the swarm methods together with a critical analysis of the current and future perspectives in the field.
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Ensemble method based on Artificial Neural Networks to estimate air pollution health risks

TL;DR: This research aims to apply 10 distinct ANN and 4 ensemble to estimate hospital admissions for respiratory diseases caused by particulate matter and meteorological variables of Campinas and Sao Paulo cities, Brazil, and new proposal of GLM was introduced, considering coefficients calculation via particle swarm optimization and seasonality via normalization procedure.
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A novel binary artificial bee colony algorithm

TL;DR: A novel artificial bee colony algorithm, named NBABC, features a mechanism which limits the number of dimensions that can be changed in the employed and onlookers bees’ phase, which outperformed not only the binary-based ABCs but also the other binary swarm-based and evolutionary-based optimizers.
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Sensors and Systems for Physical Rehabilitation and Health Monitoring-A Review.

TL;DR: This paper presents a state-of-the-art review of sensors and systems for rehabilitation and health monitoring based on three groups: Sensors in Healthcare, Home Medical Assistance, and Continuous Health Monitoring; Systems and sensors in Physical Rehabilitation; and Assistive Systems.