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ORCIDRenato A. Krohling
Renato Antonio Krohling
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- affiliation: Federal University of Espirito Santo, Vitória, Brazil
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2020 – today
- 2024
[j27]André Luiz Netto Casotti, Renato A. Krohling:
A multi-objective formulation for the team formation problem using Krippendorff's disagreement and sociometric cohesion with Pareto-solutions obtained via evolutionary algorithms. Comput. Oper. Res. 161: 106444 (2024)- [j26]Beatriz Matias Santana Maia
, Maria Clara Falcão Ribeiro de Assis, Leandro M. de Lima, Matheus Becali Rocha, Humberto Giuri Calente, Maria Luiza Armini Correa, Danielle Resende Camisasca, Renato Antonio Krohling:
Transformers, convolutional neural networks, and few-shot learning for classification of histopathological images of oral cancer. Expert Syst. Appl. 241: 122418 (2024) - [j25]Matheus Becali Rocha, Renato A. Krohling:
VAE-GNA: a variational autoencoder with Gaussian neurons in the latent space and attention mechanisms. Knowl. Inf. Syst. 66(10): 6415-6437 (2024) - [j24]Carlos A. S. da Silva, Renato A. Krohling:
An extreme learning machine algorithm for semi-supervised classification of unbalanced data streams with concept drift. Multim. Tools Appl. 83(13): 37549-37588 (2024) - [i19]Flavio P. Loss, Pedro H. da Cunha, Matheus Becali Rocha, Madson Poltronieri Zanoni, Leandro M. de Lima, Isadora Tavares Nascimento, Isabella Rezende, Tania R. P. Canuto, Luciana de Paula Vieira, Renan Rossoni, Maria C. S. Santos, Patricia Lyra Frasson, Wanderson Romão, Paulo R. Filgueiras, Renato A. Krohling:
Skin cancer diagnosis using NIR spectroscopy data of skin lesions in vivo using machine learning algorithms. CoRR abs/2401.01200 (2024) - 2023
- [i18]Breno Aguiar Krohling, Renato A. Krohling:
1D Convolutional neural networks and machine learning algorithms for spectral data classification with a case study for Covid-19. CoRR abs/2301.10746 (2023) - [i17]Renato A. Krohling:
Bayesian artificial brain with ChatGPT. CoRR abs/2308.14732 (2023) - 2022
- [c34]Leandro M. de Lima, Renato A. Krohling:
Exploring Advances in Transformers and CNN for Skin Lesion Diagnosis on Small Datasets. BRACIS (2) 2022: 282-296 - [c33]Eduarda C. Coppo, Rhuan S. Caetano, Leandro M. de Lima, Renato A. Krohling:
Student Dropout Prediction using 1D CNN-LSTM with Variational Autoencoder Oversampling. LA-CCI 2022: 1-6 - [i16]José G. M. Esgario, Renato A. Krohling:
Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data. CoRR abs/2201.10650 (2022) - [i15]Pedro P. Ladeira, Leandro M. de Lima, Renato A. Krohling:
A visualization tool for data analysis on higher education dropout: a case study at UFES. CoRR abs/2201.12637 (2022) - [i14]Renato A. Krohling, Erick R. F. A. Schneider:
Solving integer multi-objective optimization problems using TOPSIS, Differential Evolution and Tabu Search. CoRR abs/2204.02522 (2022) - [i13]Leandro M. de Lima, Renato A. Krohling:
Exploring Advances in Transformers and CNN for Skin Lesion Diagnosis on Small Datasets. CoRR abs/2205.15442 (2022) - [i12]Elivelto Ebermam, Helder Knidel, Renato A. Krohling:
Development of a hybrid method for stock trading based on TOPSIS, EMD and ELM. CoRR abs/2206.06723 (2022) - 2021
- [j23]Lucas M. Tassis, João E. Tozzi de Souza, Renato A. Krohling:
A deep learning approach combining instance and semantic segmentation to identify diseases and pests of coffee leaves from in-field images. Comput. Electron. Agric. 186: 106191 (2021) - [j22]André G. C. Pacheco, Renato A. Krohling:
An Attention-Based Mechanism to Combine Images and Metadata in Deep Learning Models Applied to Skin Cancer Classification. IEEE J. Biomed. Health Informatics 25(9): 3554-3563 (2021) - [c32]Leandro M. de Lima, Renato A. Krohling:
Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learning. IJCNN 2021: 1-8 - [i11]Leandro M. de Lima, Renato A. Krohling:
Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learning. CoRR abs/2104.10258 (2021) - [i10]Breno Krohling, Pedro B. C. Castro, André G. C. Pacheco, Renato A. Krohling:
A Smartphone based Application for Skin Cancer Classification Using Deep Learning with Clinical Images and Lesion Information. CoRR abs/2104.14353 (2021) - 2020
- [j21]André G. C. Pacheco, Renato A. Krohling:
The impact of patient clinical information on automated skin cancer detection. Comput. Biol. Medicine 116: 103545 (2020) - [j20]José G. M. Esgario, Renato A. Krohling, Jose A. Ventura:
Deep learning for classification and severity estimation of coffee leaf biotic stress. Comput. Electron. Agric. 169: 105162 (2020) - [c31]André G. C. Pacheco, Chandramouli Shama Sastry, Thomas Trappenberg, Sageev Oore, Renato A. Krohling:
On Out-of-Distribution Detection Algorithms with Deep Neural Skin Cancer Classifiers. CVPR Workshops 2020: 3152-3161 - [c30]Pedro B. C. Castro, Breno Krohling, André G. C. Pacheco, Renato A. Krohling:
An app to detect melanoma using deep learning: An approach to handle imbalanced data based on evolutionary algorithms. IJCNN 2020: 1-6 - [c29]André G. C. Pacheco, Thomas Trappenberg, Renato A. Krohling:
Learning dynamic weights for an ensemble of deep models applied to medical imaging classification. IJCNN 2020: 1-8
2010 – 2019
- 2019
- [j19]Renato A. Krohling, André G. C. Pacheco, Guilherme Artem dos Santos:
TODIM and TOPSIS with Z-numbers. Frontiers Inf. Technol. Electron. Eng. 20(2): 283-291 (2019) - [c28]Antonio Eloy de Oliveira Araujo, Renato Antonio Krohling:
A Multiobjective Genetic Approach with TOPSIS to Learn Fuzzy Rule-Based Classification Systems. BRACIS 2019: 54-59 - [c27]Antonio Eloy de Oliveira Araujo, Renato Antonio Krohling:
Generating a Fuzzy Rule Based Classification System by genetic learning of granularity level using TOPSIS. EUSFLAT Conf. 2019 - [c26]Gabriel De Angelo, André G. C. Pacheco, Renato A. Krohling:
Skin lesion segmentation using deep learning for images acquired from smartphones. IJCNN 2019: 1-8 - [c25]Carlos A. S. da Silva, Renato A. Krohling:
Semi-Supervised Online Elastic Extreme Learning Machine with Forgetting Parameter to deal with concept drift in data streams. IJCNN 2019: 1-8 - [i9]José G. M. Esgario, Iago E. da Silva, Renato A. Krohling:
Application of Genetic Algorithms to the Multiple Team Formation Problem. CoRR abs/1903.03523 (2019) - [i8]Giuliano L. Manso, Helder Knidel, Renato A. Krohling, Jose A. Ventura:
A smartphone application to detection and classification of coffee leaf miner and coffee leaf rust. CoRR abs/1904.00742 (2019) - [i7]José G. M. Esgario, Renato A. Krohling, Jose A. Ventura:
Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress. CoRR abs/1907.11561 (2019) - [i6]André G. C. Pacheco, Renato A. Krohling:
The impact of patient clinical information on automated skin cancer detection. CoRR abs/1909.12912 (2019) - [i5]André G. C. Pacheco, Renato A. Krohling:
Recent advances in deep learning applied to skin cancer detection. CoRR abs/1912.03280 (2019) - 2018
- [j18]André G. C. Pacheco, Renato A. Krohling, Carlos A. S. da Silva:
Restricted Boltzmann machine to determine the input weights for extreme learning machines. Expert Syst. Appl. 96: 77-85 (2018) - [j17]André G. C. Pacheco, Renato A. Krohling:
Aggregation of neural classifiers using Choquet integral with respect to a fuzzy measure. Neurocomputing 292: 151-164 (2018) - [c24]Elivelto Ebermam, Gabriel De Angelo, Helder Knidel, Renato A. Krohling:
Empirical Mode Decomposition, Extreme Learning Machine and Long Short-Term Memory for Time Series Prediction: A Comparative Study. BRACIS 2018: 492-497 - [c23]José G. M. Esgario, Renato A. Krohling:
Clustering with Minimum Spanning Tree using TOPSIS with Multi-Criteria Information. FUZZ-IEEE 2018: 1-7 - [c22]Iago E. da Silva, Renato A. Krohling:
A Fuzzy Sociometric Approach to Human Resource Allocation. FUZZ-IEEE 2018: 1-8 - [c21]André G. C. Pacheco, Renato A. Krohling:
An approach to improve online sequential extreme learning machines using restricted Boltzmann machines. IJCNN 2018: 1-8 - [c20]Carlos A. S. da Silva, Renato A. Krohling:
Semi-Supervised Online Elastic Extreme Learning Machine for Data Classification. IJCNN 2018: 1-8 - 2017
- [j16]Rodolfo Lourenzutti, Renato A. Krohling, Marek Z. Reformat:
Choquet based TOPSIS and TODIM for dynamic and heterogeneous decision making with criteria interaction. Inf. Sci. 408: 41-69 (2017) - [i4]André G. C. Pacheco, Renato A. Krohling, Carlos A. S. da Silva:
Restricted Boltzmann machine to determine the input weights for extreme learning machines. CoRR abs/1708.05376 (2017) - 2016
- [j15]Rodolfo Lourenzutti, Renato A. Krohling:
A generalized TOPSIS method for group decision making with heterogeneous information in a dynamic environment. Inf. Sci. 330: 1-18 (2016) - [j14]Mauro Campos, Renato A. Krohling:
Entropy-based bare bones particle swarm for dynamic constrained optimization. Knowl. Based Syst. 97: 203-223 (2016) - [i3]Davi E. N. Frossard, Igor O. Nunes, Renato A. Krohling:
An approach to dealing with missing values in heterogeneous data using k-nearest neighbors. CoRR abs/1608.04037 (2016) - [i2]Renato A. Krohling, Guilherme Artem dos Santos, André G. C. Pacheco:
TODIM and TOPSIS with Z-numbers. CoRR abs/1609.05705 (2016) - [i1]André G. C. Pacheco, Renato A. Krohling:
Ranking of classification algorithms in terms of mean-standard deviation using A-TOPSIS. CoRR abs/1610.06998 (2016) - 2015
- [j13]Renato A. Krohling, Rodolfo Lourenzutti, Mauro Campos:
Ranking and comparing evolutionary algorithms with Hellinger-TOPSIS. Appl. Soft Comput. 37: 217-226 (2015) - [c19]Renato A. Krohling, André G. C. Pacheco:
A-TOPSIS - An Approach Based on TOPSIS for Ranking Evolutionary Algorithms. ITQM 2015: 308-317 - [c18]Rodolfo Lourenzutti, Renato A. Krohling:
TODIM Based Method to Process Heterogeneous Information. ITQM 2015: 318-327 - [c17]Felipe Luchi, Renato A. Krohling:
Differential Evolution and Nelder-mead for Constrained Non-linear Integer Optimization Problems. ITQM 2015: 668-677 - [c16]Rafael Hrasko, André G. C. Pacheco, Renato A. Krohling:
Time Series Prediction Using Restricted Boltzmann Machines and Backpropagation. ITQM 2015: 990-999 - 2014
- [j12]Rodolfo Lourenzutti, Renato A. Krohling:
The Hellinger distance in Multicriteria Decision Making: An illustration to the TOPSIS and TODIM methods. Expert Syst. Appl. 41(9): 4414-4421 (2014) - [j11]Erick R. F. A. Schneider, Renato A. Krohling:
A hybrid approach using TOPSIS, Differential Evolution, and Tabu Search to find multiple solutions of constrained non-linear integer optimization problems. Knowl. Based Syst. 62: 47-56 (2014) - [j10]Mauro Campos, Renato A. Krohling, Ivan Enriquez:
Bare Bones Particle Swarm Optimization With Scale Matrix Adaptation. IEEE Trans. Cybern. 44(9): 1567-1578 (2014) - [c15]Mauro Campos, Renato A. Krohling:
Bare bones particle swarm with scale mixtures of Gaussians for dynamic constrained optimization. IEEE Congress on Evolutionary Computation 2014: 202-209 - [c14]Renato A. Krohling, André G. C. Pacheco:
Interval-valued Intuitionistic Fuzzy TODIM. ITQM 2014: 236-244 - 2013
- [j9]Rodolfo Lourenzutti, Renato A. Krohling:
A study of TODIM in a intuitionistic fuzzy and random environment. Expert Syst. Appl. 40(16): 6459-6468 (2013) - [j8]Renato A. Krohling, André G. C. Pacheco, André L. T. Siviero:
IF-TODIM: An intuitionistic fuzzy TODIM to multi-criteria decision making. Knowl. Based Syst. 53: 142-146 (2013) - [c13]Mauro Campos, Renato A. Krohling:
Hierarchical bare bones particle swarm for solving constrained optimization problems. IEEE Congress on Evolutionary Computation 2013: 805-812 - 2012
- [j7]Fábio Fabris, Renato A. Krohling:
A co-evolutionary differential evolution algorithm for solving min-max optimization problems implemented on GPU using C-CUDA. Expert Syst. Appl. 39(12): 10324-10333 (2012) - [j6]Renato A. Krohling, Talles T. M. de Souza:
Combining prospect theory and fuzzy numbers to multi-criteria decision making. Expert Syst. Appl. 39(13): 11487-11493 (2012) - [j5]Gilberto A. S. Segundo, Renato A. Krohling, Rodrigo C. Cosme:
A differential evolution approach for solving constrained min-max optimization problems. Expert Syst. Appl. 39(18): 13440-13450 (2012) - 2011
- [j4]Renato A. Krohling, Vinicius C. Campanharo:
Fuzzy TOPSIS for group decision making: A case study for accidents with oil spill in the sea. Expert Syst. Appl. 38(4): 4190-4197 (2011) - [j3]Eduardo Mendel, Renato A. Krohling, Mauro Campos:
Swarm algorithms with chaotic jumps applied to noisy optimization problems. Inf. Sci. 181(20): 4494-4514 (2011) - 2010
- [c12]Lucas de Paula Veronese, Renato A. Krohling:
Differential evolution algorithm on the GPU with C-CUDA. IEEE Congress on Evolutionary Computation 2010: 1-7
2000 – 2009
- 2009
- [c11]Lucas de Paula Veronese, Renato A. Krohling:
Swarm's flight: Accelerating the particles using C-CUDA. IEEE Congress on Evolutionary Computation 2009: 3264-3270 - [c10]Renato A. Krohling, Eduardo Mendel:
Bare Bones Particle Swarm Optimization with Gaussian or Cauchy jumps. IEEE Congress on Evolutionary Computation 2009: 3285-3291 - [c9]Renato A. Krohling, Mauro Campos, Patrick Borges:
Bare Bones Particle Swarm Applied to Parameter Estimation of Mixed Weibull Distribution. WSC 2009: 53-60 - 2006
- [j2]Renato A. Krohling, Leandro dos Santos Coelho:
Coevolutionary Particle Swarm Optimization Using Gaussian Distribution for Solving Constrained Optimization Problems. IEEE Trans. Syst. Man Cybern. Part B 36(6): 1407-1416 (2006) - [c8]Renato A. Krohling, Leandro dos Santos Coelho:
PSO-E: Particle Swarm with Exponential Distribution. IEEE Congress on Evolutionary Computation 2006: 1428-1433 - [c7]Leandro dos Santos Coelho, Renato A. Krohling:
Nonlinear System Identification Based on B-Spline Neural Network and Modified Particle Swarm Optimization. IJCNN 2006: 3748-3753 - 2005
- [c6]Renato A. Krohling:
Gaussian particle swarm with jumps. Congress on Evolutionary Computation 2005: 1226-1231 - [c5]Andi Asmara, Renato A. Krohling, Frank Hoffmann:
Parameter tuning of a computed-torque controller for a 5 degree of freedom robot arm using co-evolutionary particle swarm optimization. SIS 2005: 162-168 - 2004
- [c4]Renato A. Krohling, Frank Hoffmann, Leandro dos Santos Coelho:
Co-evolutionary particle swarm optimization for min-max problems using Gaussian distribution. IEEE Congress on Evolutionary Computation 2004: 959-964 - 2002
- [c3]Leandro dos Santos Coelho, Antonio Augusto Rodrigues Coelho, Renato A. Krohling:
Parameters tuning of multivariable controllers based on memetic algorithm: fundamentals and application. ISIC 2002: 752-757 - [c2]Yuhui Shi, Renato A. Krohling:
Co-evolutionary particle swarm optimization to solve min-max problems. IEEE Congress on Evolutionary Computation 2002: 1682-1687 - [c1]Renato A. Krohling, Helder Knidel, Yuhui Shi:
Solving numerical equations of hydraulic problems using particle swarm optimization. IEEE Congress on Evolutionary Computation 2002: 1688-1690 - 2001
- [j1]Renato A. Krohling, Joost P. Rey:
Design of optimal disturbance rejection PID controllers using genetic algorithms. IEEE Trans. Evol. Comput. 5(1): 78-82 (2001)
1990 – 1999
- 1999
- [b1]Renato A. Krohling:
Entwicklung von neuen Verfahren zum Reglerentwurf mit Hilfe von genetischen Algorithmen. Saarland University, Saarbrücken, Germany, 1999, pp. 1-99
Coauthor Index
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