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Renato 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
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
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
An extreme learning machine algorithm for semi-supervised classification of unbalanced data streams with concept drift. Multim. Tools Appl. 83(13): 37549-37588 (2024) - [i20]Flavio P. Loss, Pedro H. da Cunha, Matheus Becali Rocha
Skin cancer diagnosis using NIR spectroscopy data of skin lesions in vivo using machine learning algorithms. CoRR abs/2401.01200 (2024) - [i19]Humberto Giuri Calente, Renato A. Krohling:
CBIDR: A novel method for information retrieval combining image and data by means of TOPSIS applied to medical diagnosis. CoRR abs/2410.06180 (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
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
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
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
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
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
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
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
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
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
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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