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Rui Mendes 0001
Person information
- affiliation: University of Minho, Department of Informatics, Braga, Portugal
Other persons with the same name
- Rui Mendes 0002 — University of Porto, Portugal
- Rui Mendes 0003
(aka: Rui Sousa Mendes, Rui Manuel Sousa Mendes) — Polytechnic Institute of Coimbra, Portugal
- Rui Mendes 0004 — University of Coimbra, Department of Informatics Engineering, CISUC, Portugal
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2020 – today
- 2023
- [j9]Luís Miguel Matos
A Deep Learning-Based Decision Support System for Mobile Performance Marketing. Int. J. Inf. Technol. Decis. Mak. 22(2): 679-703 (2023) - 2022
- [j8]Luís Miguel Matos
Categorical Attribute traNsformation Environment (CANE): A python module for categorical to numeric data preprocessing. Softw. Impacts 13: 100359 (2022) - [c27]Kévin Martins
Cherry-Picking Meta-heuristic Algorithms and Parameters for Real Optimization Problems. EPIA 2022: 500-511 - 2021
- [j7]Pedro José Pereira
Multi-objective Grammatical Evolution of Decision Trees for Mobile Marketing user conversion prediction. Expert Syst. Appl. 168: 114287 (2021) - [c26]Rui Castro Mendes:
Moopec: A Tool for Creating Programming Problems. ICPEC 2021: 9:1-9:7 - [c25]Rui Castro Mendes, José João Almeida
A Teaching Assistant for the C Language (Short Paper). ICPEC 2021: 13:1-13:8 - [c24]João Correia
Neural Complexity Assessment: A Deep Learning Approach to Readability Classification for European Portuguese Corpora. IDEAL 2021: 300-311 - 2020
- [j6]Paulo Cortez
Multi-step time series prediction intervals using neuroevolution. Neural Comput. Appl. 32(13): 8939-8953 (2020) - [c23]Guilherme Fernandes, Nuno Oliveira, Paulo Cortez
A realistic scooter rebalancing system via metaheuristics. GECCO Companion 2020: 265-266
2010 – 2019
- 2019
- [c22]Pedro José Pereira
Using Neuroevolution for Predicting Mobile Marketing Conversion. EPIA (2) 2019: 373-384 - [c21]Luís Miguel Matos
Using Deep Learning for Ordinal Classification of Mobile Marketing User Conversion. IDEAL (1) 2019: 60-67 - [c20]Luís Miguel Matos
Using Deep Learning for Mobile Marketing User Conversion Prediction. IJCNN 2019: 1-8 - [c19]José João Almeida
Hunting Ancestors: A Unified Approach for Discovering Genealogical Information. SLATE 2019: 22:1-22:6 - 2018
- [j5]Orlando Rocha, Rui Mendes
JBiclustGE: Java API with unified biclustering algorithms for gene expression data analysis. Knowl. Based Syst. 155: 83-87 (2018) - [c18]Luís Miguel Matos
A Comparison of Data-Driven Approaches for Mobile Marketing User Conversion Prediction. IEEE Conf. on Intelligent Systems 2018: 140-146 - [c17]Rui Mendes
eOS: The Exercise Operating System. SLATE 2018: 5:1-5:13 - [c16]Susana Silva
A Categorical Clustering of Publishers for Mobile Performance Marketing. SOCO-CISIS-ICEUTE 2018: 145-154 - 2017
- [c15]Pedro José Pereira
Multi-objective Learning of Neural Network Time Series Prediction Intervals. EPIA 2017: 561-572 - 2015
- [c14]Rafael T. Pereira
Reconstructing transcriptional Regulatory Networks using data integration and Text Mining. BIBM 2015: 1552-1558 - 2014
- [j4]Miguel Rocha
Optimization of fed-batch fermentation processes with bio-inspired algorithms. Expert Syst. Appl. 41(5): 2186-2195 (2014) - [c13]Orlando Rocha, Paulo Vilaça
Reg4OptFlux: an OptFlux plug-in that comprises meta-heuristics approaches for Metabolic engineering using integrated models. NaBIC 2014: 226-231 - 2013
- [c12]Rafael T. Pereira
Current Trends in Bio-Ontologies and Data Integration. DCAI 2013: 579-586
2000 – 2009
- 2008
- [j3]Miguel Rocha
Natural computation meta-heuristics for the in silico optimization of microbial strains. BMC Bioinform. 9 (2008) - [c11]Rui Mendes
A framework for the integrated analysis of metabolic and regulatory networks. BIBE 2008: 1-6 - 2007
- [c10]Rui Mendes, James Kennedy:
Stochastic Barycenters and Beta Distribution for Gaussian Particle Swarms. EPIA Workshops 2007: 259-270 - [c9]Miguel Rocha
Evaluating Simulated Annealing Algorithms in the Optimization of Bacterial Strains. EPIA Workshops 2007: 473-484 - [c8]Miguel Rocha
A platform for the selection of genes in DNA microarraydata using evolutionary algorithms. GECCO 2007: 415-423 - 2006
- [j2]James Kennedy, Rui Mendes
Neighborhood topologies in fully informed and best-of-neighborhood particle swarms. IEEE Trans. Syst. Man Cybern. Syst. 36(4): 515-519 (2006) - [c7]Rui Mendes, Isabel Rocha, Eugénio C. Ferreira, Miguel Rocha:
A Comparison of Algorithms for the Optimization of Fermentation Processes. IEEE Congress on Evolutionary Computation 2006: 2018-2025 - 2005
- [c6]Arvind S. Mohais, Rui Mendes
Neighborhood Re-structuring in Particle Swarm Optimization. Australian Conference on Artificial Intelligence 2005: 776-785 - [c5]Rui Mendes, Arvind S. Mohais:
DynDE: a differential evolution for dynamic optimization problems. Congress on Evolutionary Computation 2005: 2808-2815 - 2004
- [j1]Rui Mendes
The Fully Informed Particle Swarm: Simpler, Maybe Better. IEEE Trans. Evol. Comput. 8(3): 204-210 (2004) - [c4]Rui Mendes, José Neves:
What Makes a Successful Society? Experiments with Population Topologies in Particle Swarms. SBIA 2004: 346-355 - 2003
- [c3]Rui Mendes
Watch thy neighbor or how the swarm can learn from its environment. SIS 2003: 88-94 - 2002
- [c2]James Kennedy, Rui Mendes
Population structure and particle swarm performance. IEEE Congress on Evolutionary Computation 2002: 1671-1676 - 2001
- [c1]Miguel Rocha, Rui Mendes, Paulo Cortez, José Neves:
Sitting guests at a wedding party: experiments on genetic and evolutionary constrained optimization. CEC 2001: 671-678
Coauthor Index
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