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Detalhes

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Publicações

2023

Ant-Balanced Multiple Traveling Salesmen: ACO-BmTSP

Autores
Pereira, SD; Pires, EJS; Oliveira, PBD;

Publicação
ALGORITHMS

Abstract
A new algorithm based on the ant colony optimization (ACO) method for the multiple traveling salesman problem (mTSP) is presented and defined as ACO-BmTSP. This paper addresses the problem of solving the mTSP while considering several salesmen and keeping both the total travel cost at the minimum and the tours balanced. Eleven different problems with several variants were analyzed to validate the method. The 20 variants considered three to twenty salesmen regarding 11 to 783 cities. The results were compared with best-known solutions (BKSs) in the literature. Computational experiments showed that a total of eight final results were better than those of the BKSs, and the others were quite promising, showing that with few adaptations, it will be possible to obtain better results than those of the BKSs. Although the ACO metaheuristic does not guarantee that the best solution will be found, it is essential in problems with non-deterministic polynomial time complexity resolution or when used as an initial bound solution in an integer programming formulation. Computational experiments on a wide range of benchmark problems within an acceptable time limit showed that compared with four existing algorithms, the proposed algorithm presented better results for several problems than the other algorithms did.

2022

Forecasting Student s Dropout: A UTAD University Study

Autores
Da Silva, DEM; Pires, EJS; Reis, A; Oliveira, PBD; Barroso, J;

Publicação
FUTURE INTERNET

Abstract
In Portugal, the dropout rate of university courses is around 29%. Understanding the reasons behind such a high desertion rate can drastically improve the success of students and universities. This work applies existing data mining techniques to predict the academic dropout mainly using the academic grades. Four different machine learning techniques are presented and analyzed. The dataset consists of 331 students who were previously enrolled in the Computer Engineering degree at the Universidade de Tras-os-Montes e Alto Douro (UTAD). The study aims to detect students who may prematurely drop out using existing methods. The most relevant data features were identified using the Permutation Feature Importance technique. In the second phase, several methods to predict the dropouts were applied. Then, each machine learning technique's results were displayed and compared to select the best approach to predict academic dropout. The methods used achieved good results, reaching an Fl-Score of 81% in the final test set, concluding that students' marks somehow incorporate their living conditions.

2022

REVIEW OF ENERGY AUDIT AND BENCHMARKING TOOLS TO STUDY ENERGY EFFICIENCY THROUGH REDUCING CONSUMPTION IN WASTEWATER TREATMENT SYSTEMS

Autores
Esteves, F; Cardoso, JC; Leitão, S; Pires, E; Baptista, J;

Publicação
Cadernos de Educação Tecnologia e Sociedade

Abstract
Wastewater treatment systems are major consumers of electricity being responsible for 3 to 5% of global energy consumption, and 56% of greenhouse gas emissions into the atmosphere in the water treatment sector. Climate change currently imposes the definition of a new pattern of human behavior in the defense and sharing of a common space that is the planet, so the optimization of water treatment models plays a crucial role in the definition of sustainability strategies as part of the challenges for decarbonization by 2050. The physical-chemical characteristics of the influent, the treatment techniques and associated technologies and the unpredictability of external phenomena of inefficiency transform wastewater treatment plants (WWTPs) into complex systems, sometimes difficult to understand. The study of energy efficiency plays an important role in the emergence of a standard behavior model, which allows the correction of unbalanced situations in the expected energy consumption. Given the importance of the topic, the present review aims to study energy auditing techniques and benchmarking tools developed for the wastewater treatment sector to reduce the current electricity consumption, which could represent up to 90% of total energy consumption. The result of the research was organized according to the criteria defined for the characterization of auditing techniques and benchmarking tools. A review was conducted from 51 scientific papers from different reference research platforms published in the last 20 years according to the keywords. This literature review has shown that there are, in the classification of consumption reduction, energy auditing and benchmarking tools; energy management techniques and methods directed to the energy efficiency of the treatment stages and specific equipment; and, finally, decision support tools. According to the methodology followed, it was possible to conclude that although the concern is not recent, there are techniques and tools for assessing energy performance more suitable for the wastewater sector. However, the authors recognize that associated with the complexity of wastewater treatment systems, inefficiency phenomena still strongly impact energy efficiency assessment, so the contributions for their identification and quantification may represent an added value for data analysis, systematization, and optimization methodologies.

2022

A Hybrid Approach GABC-LS to Solve mTSP

Autores
Castro Pereira, Sd; Solteiro Pires, EJ; Moura Oliveira, PBd;

Publicação
Optimization, Learning Algorithms and Applications - Second International Conference, OL2A 2022, Póvoa de Varzim, Portugal, October 24-25, 2022, Proceedings

Abstract

2022

How can we predict the kidney graft failure of Portuguese patients?

Autores
Cerqueira, S; Campelos, MR; Leite, A; Pires, EJS; Pereira, LT; Diniz, H; Sampaio, S; Figueiredo, A; Alve, R;

Publicação
REVISTA DE NEFROLOGIA DIALISIS Y TRASPLANTE

Abstract
Background: The gap between offer and need for a kidney transplant (KT) has been increasing. The Kidney Donor Profile Index (KDPI) is a measure of organ quality and allows estimation of graft survival, but could not apply to all populations. Knowledge of our kidney donor and recipient population is vital to adjust transplant strategies. Methods: We performed a retrospective evaluation of donors and recipients of KT regarding two kidney transplant units: Centro Hospitalar Universitario de Coimbra, CHUC (Coimbra, Portugal) and Centro Hospitalar Universitario de Sao Joao, CHUSJ (Porto, Portugal), between 2013 and 2018. We then did statistical analysis and modeling, correlating these KT outcomes with donor and recipient characteristics, including KDPI. Artificial intelligence methods were performed to determine the best predictors of graft survival. Results: We analyzed a total of 808 kidney donors and 829 recipients of KT. The association between KDPI and graft dysfunction was only moderate. The decision tree machine learning algorithm proved to be better at predicting graft failure than artificial neural networks. Multinomial logistic regression revealed recipient age as an important prognostic factor for graft loss. Conclusions: In this Portuguese cohort, KDPI was not a good measure of KT survival, although it correlated with GFR 1 year post-transplant. The decision tree proved to be the best algorithm to predict graft failure. Age of the recipient was the most important predictor of graft dysfunction.

Teses
supervisionadas

2022

AI-based collaborative robotic system to support physiotherapy interventions

Autor
Cláudia Daniela Costa Rocha

Instituição
UTAD

2022

Técnicas de aprendizagem máquina aplicadas à covid-19

Autor
Milene Sofia Alves Fraga

Instituição
UTAD

2022

Classificação de doenças pulmonares obstrutivas crónicas

Autor
Inês de Almeida

Instituição
UTAD

2021

Filogenia mitogenomica de bivalves de água doce (Bivalvia: Unionida)

Autor
João Eduardo Afonso Teiga Teixeira

Instituição

2021

Revisão de técnicas de pesquisa inspiradas em enxames

Autor
Daniel da Silva Duarte

Instituição
UTAD