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Dr. Daniel Leite
Researcher in Human-Centered AI (Link)
Faculty of Electrical Engineering, Computer Science and Math
Dept. of Computer Science (Data Science DICE: Link)
Paderborn University, 33098 Paderborn, Germany
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Room: TP6.3.109 (Technologiepark 6)
E-mail: daniel.leite@uni-paderborn.de; danfl7@gmail.com
Initiative on Brain Controlled Technologies: Link
Areas:
Incremental machine learning
Human-Centered AI
Neural networks
Adaptive Fuzzy systems
Control theory
Granular computing
I am a researcher in the Department of Computer Science, Data Science (DICE) Group, Paderborn University, Germany (as of November 2023).
For 11 years I was a professor and researcher at the UFLA and UFMG, Brazil; and UAI, Chile, in the areas of dynamic systems, fuzzy systems, neural networks, data mining, and control theory.
I earned a PhD from the State University of Campinas, UNICAMP, Brazil, 2012, and was a postdoctoral fellow at the University of Ljubljana, UL, Slovenia, 2018-2019, and Federal University of Minas Gerais, UFMG, Brazil, 2013-2014.
My research interests are at the interface of human-centered systems, machine learning, and control systems. A particular emphasis is on the development of new perspectives for the study of data uncertainty as well as on new methodologies to analyze and detect patterns in data streams for decision support and forecasting. We have combined ideas and concepts of evolving intelligence for continual learning, granular computing, and convolutional neural networks. Applications include prediction of meteorological phenomena; image-based navigation and reasoning of autonomous robots in unknown environments; classification of image and interval data streams; early detection of medical disorders, control of engineering systems, and fault detection for predictive maintenance.
Distinction:
- 2023 Stanford top 2% most influential scientists (AI & Image Processing #6471)
- 2021 Alysson Paulinelli Award for the paper with the highest impact factor of the year, UFLA, Minas Gerais, Brazil
- 2019 IEEE Senior Member
- 2017 NAFIPS Early Career Award
- 2017 IEEE CIS Outstanding PhD Dissertation Award - IEEE Computational Intelligence Society, Naples, Italy
- 2015 Best PhD Thesis Award - North American Fuzzy Information Processing Society, NAFIPS, Washington, D.C., US
- 2014 Best PhD Thesis in Artificial Intelligence - Brazilian Computer Society, SBC, São Carlos, São Paulo, Brazil
- 2012 IEEE CIS Outstanding Student Paper - IEEE World Congress on Computational Intelligence, Brisbane, Australia
- 2009 IEEE CIS Outstanding Student Paper - IEEE International Joint Conference on Neural Networks, Atlanta, US
Distinction as advisor:
- 2020 Best Student Paper Finalist at the IEEE World Congress on Computational Intelligence, Glasgow, UK. To: Leticia Decker, with Daniel Leite, Luca Giommi, and Daniele Bonacorsi
- 2019 Best PhD Thesis Award, The International Biometric Society, RBras'19, Cuiabá, Brazil. To: Vania Mota, with Ednilton Tavares, and Daniel Leite
- 2018 Outstanding Student Paper at the IEEE International Conference on Fuzzy Systems, Rio de Janeiro, Brazil. To: Eduardo Soares, with Heloisa Camargo, Suzana Camargo, and Daniel Leite
- 2018 Best Paper Presentation Award, Meeting of the Brazilian Society of Computational and Applied Mathematics, SBMAC, Lavras, Brazil. To: Stella Lamounier, with Marcio Santana, Eduardo Soares, and Daniel Leite
On YouTube
Presentation (11/2021): EEG-based Emotion Recognition in Computer Games (YouTube Link)
Presentation (09/2021): Battery Charge Capacity Prediction (YouTube Link)
Presentation (06/2021): Evolving LMI Fuzzy Control of Unknown Systems (YouTube Link)
Presentation (07/2020): Fuzzy Classifier from Never-Ending Semi-Supervised Data Streams (Youtube Link)
Research Group (10/2019): Initiative on Brain Controlled Technologies (Link)
Publications
2024
[109] Leite, D., Silva, A., Casalino, G., Sharma, A., Fortunato, D., Ngomo, A.-C. (2024). EGNN-C+: Interpretable Evolving Granular Neural Network and Application in Classification of Weakly-Supervised EEG Data Streams. IEEE International Conference on Evolving and Adaptive Intelligent Systems, EAIS, 10p. Madrid, Spain, 2024.
Arxiv: https://arxiv.org/abs/2402.17792
[108] Sharma, A., Leite, D., Demir, C., Ngomo, A.-C. (2024). Trading-Off Interpretability and Accuracy in Medical Applications: A Study toward Optimal Explainability of Heoffding Trees. World Congress on Computational Intelligence, WCCI - FUZZ-IEEE, 10p. Yokohama, Japan, 2024.
[107] Leite, D., Sharma, A., Demir, C., Ngomo, A.-C. (2024). Interpretability Index based on Balanced Volumes for Transparent Models and Agnostic Explainers. World Congress on Computational Intelligence, WCCI - FUZZ-IEEE, 10p. Yokohama, Japan, 2024.
[106] Leite, D., Casalino, G., Kaczmarek-Majer, K., Castellano, G. (2023). Submitted journal. 35p. TBA.
2023
[105] Leite, D., Gomide, F. (2023). Data Driven Fuzzy and Neural Dynamic Systems Modeling. International Conference on Machine Learning and Cybernetics (ICMLC), Australia, 321-325, 2023. doi: 10.1109/ICMLC58545.2023.10327983
https://ieeexplore.ieee.org/document/10327983
[104] Angelov, P., Filev, D., Kasabov, N., Leite, D., Pratama, M., Saminger-Platz, S., Klement, E. P. (2023). Obituary Edwin Lughofer (1972-2023). Evolving Systems, 14, 747-748, 2023. doi: 10.1007/s12530-023-09538-5
https://link.springer.com/article/10.1007/s12530-023-09538-5
[103] Casalino, G., Castellano, G., Hryniewicz, O., Leite, D., Opara, K., Radziszewska, W., Kacmarek-Majer, K. (2023). Semi-Supervised vs. Supervised Learning for Mental Health Monitoring: A Case Study on Bipolar Disorder. International Journal of Applied Mathematics and Computer Science, 33(3), pp. 419-428, 2023. doi: 10.34768/amcs-2023-0030
https://www.amcs.uz.zgora.pl/?action=paper&paper=1714
[102] Leite, D., Skrjanc, I., Blazic, S., Zdesar, A., Gomide, F. (2023). Interval Incremental Learning of Interval Data Streams and Application to Vehicle Tracking. Information Sciences, 630, 1-22, 2023. doi:10.1016/j.ins.2023.02.027
https://www.sciencedirect.com/science/article/pii/S0020025523002165
[101] Kaczmarek-Majer, K., Casalino, G., Castellano, G., Leite, D., Hryniewicz, O. (2023). Fuzzy Linguistic Summaries for Explaining Online Semi-Supervised Learning. 11th IEEE International Conference on Intelligent Systems, 8p. Warsaw, Poland, 2022. doi:10.1109/IS57118.2022.10019636
https://ieeexplore.ieee.org/document/10019636
[100] Casalino, G., Castellano, G., Kaczmarek-Majer, K., Leite, D. (2023). Online Learning from Uncertain Data Streams: Editorial. 1st Workshop on Online Learning from Uncertain Data. In: IEEE World Congress on Computational Intelligence - CEUR Proceedings, Vol. 3380, 8p. Padua, Italy, 2022.
2022
[99] Leite, D. (2022). State-Space Evolving Granular Control of Unknown Dynamic Systems. 1st Workshop on Online Learning from Uncertain Data. In: IEEE World Congress on Computational Intelligence - CEUR Proceedings, Vol. 3380, 18p. Padua, Italy, 2022.
[98] Decker, L., Leite, D., Bonacorsi, D. (2022). Explainable Log Parsing and Online Interval Granular Classification from Streams of Words. IEEE World Congress on Computational Intelligence, 8p. Padua, Italy, 2022. doi:10.1109/FUZZ-IEEE55066.2022.9882710
https://ieeexplore.ieee.org/document/9882710
[97] Leite, D., Gomide, F., Yager, R. (2022). Data-Driven Fuzzy Modeling using Level Sets. IEEE World Congress on Computational Intelligence, 6p. Padua, Italy, 2022. doi:10.1109/FUZZ-IEEE55066.2022.9882555
https://ieeexplore.ieee.org/document/9882555
[96] Leite, D., Frigeri Jr., V., & Medeiros, R. (2022). Incremental Fuzzy Machine Learning for Online Classification of Emotions in Games from EEG Data Streams. In: Handbook of Computer Learning and Intelligence (Plamen Angelov (ed)), 29p. World Scientific.
https://www.worldscientific.com/doi/abs/10.1142/9789811247323_0005
[95] Decker, L., Leite, D., Minarini, F., Rossi-Tisbeni, S., & Bonacorsi, D. (2022). Unsupervised Learning and Online Anomaly Detection. International Journal of Embedded and Real-Time Communication Systems, IJERTS, 13(1), 16p. IGI Global, 2022. doi:10.4018/IJERTCS.302112
https://www.igi-global.com/gateway/article/302112
[94] Alvarenga, T., Lima, R., Simão, S., Brandão Jr., L. C., Filho, J., Alvarenga, R., Rodrigues, P., & Leite, D. (2022). Ensemble of Hybrid Bayesian Networks for Predicting the AMEn of Broiler Feedstuffs. Computers and Electronics in Agriculture, 198, 107067, 11p., July, 2022. doi:10.1016/j.compag.2022.107067
https://www.sciencedirect.com/science/article/pii/S0168169922003842?via%3Dihub
SSRN Preprints 2022
[93] Leite, D., Skrjanc, I., Blazic, S., Zdesar, A., Gomide, F. (2022). Interval Incremental Learning of Interval Data Streams and Application to Vehicle Tracking. Elsevier SSRN, 40p., Sept, 2022.
https://ssrn.com/abstract=4230544
2021
[92] Leite, D., Frigeri Jr., V., Medeiros, R. (2021). Adaptive Gaussian Fuzzy Classifier for Real-Time Emotion Recognition in Computer Games. 2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI), Temuco, Chile, 6p. doi:10.1109/LA-CCI48322.2021.9769842
https://ieeexplore.ieee.org/document/9769842
[91] Leite, D., Coutinho, P., Bessa, I., Camargos, M., Cordovil Jr., L., & Palhares, R. (2021). Incremental Learning and State-Space Evolving Fuzzy Control of Nonlinear Time-Varying Systems with Unknown Model. 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), Atlantis Studies in Uncertainty Modelling, 3, 80-87. doi:10.2991/asum.k.210827.011
https://www.atlantis-press.com/proceedings/ifsa-eusflat-agop-21/125960366
[90] Camargos, M., Bessa, I., Cordovil Jr., L., Coutinho, P., Leite, D., & Palhares, R. (2021). Evolving Fuzzy System Applied to Battery Charge Capacity Prediction for Fault Prognostics. 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), Atlantis Studies in Uncertainty Modelling, 3, 71-79. doi:10.2991/asum.k.210827.010
https://www.atlantis-press.com/proceedings/ifsa-eusflat-agop-21/125960364
[89] Aguiar, C., Leite, D., Pereira, D., Andonovski, G., & Skrjanc, I. (2021). Nonlinear Modeling and Robust LMI Fuzzy Control of Overhead Crane Systems. Journal of the Franklin Institute, 358(2), 1376-1402. doi:10.1016/j.jfranklin.2020.12.003
https://www.sciencedirect.com/science/article/abs/pii/S0016003220308000
[88] Barbosa, B., Costa Jr., P., Batalha, R., & Leite, D. (2021). Sensores Virtuais para Detecção de Descargas Parciais em Transformadores de Potência. In: Engenharia no Século XXI, 22, 95-104. doi:10.36229/978-65-5866-112-2.CAP.11
[87] Ribeiro, T., Borges, M. T., Cardoso, R., Polisel, D., Coelho, R., Leite, D., Costa, S. (2021) Classificação Fuzzy de Padrões Não-Motores e Indicação da Severidade da Doença de Parkinson. In: Desvendando a Engenharia: Sua Abrangência e Multidisciplinaridade, Cap. 17, 244-257. Científica Digital. doi:10.37885/210404191
https://www.editoracientifica.org/articles/code/210404191
ArXiv Preprints 2021
[86] Leite, D., Frigeri Jr., V., Medeiros, R. (2021). Adaptive Gaussian Fuzzy Classifier for Real-Time Emotion Recognition in Computer Games. ArXiv Preprint. doi: arXiv:2103.03488
https://arxiv.org/abs/2103.03488
[85] Leite, D., Coutinho, P. H., Bessa, I., Camargos, M., Cordovil Jr., L, Palhares, R. (2021). Incremental Learning and State-Space Evolving Fuzzy Control of Nonlinear Time-Varying Systems with Unknown Model. ArXiv Preprint. doi: arXiv:2102.09503
https://arxiv.org/abs/2102.09503
[84] Camargos, M., Bessa, I., Cordovil Jr., L, Coutinho, P. H., Leite, D., Palhares, R. (2021). Evolving Fuzzy System Applied to Battery Charge Capacity Prediction for Fault Prognostics. ArXiv Preprint. doi: arXiv:2102.09521
https://arxiv.org/abs/2102.09521
2020
[83] Leite, D., Andonovski, G., Skrjanc, I., Gomide, F. (2020). Optimal Rule-Based Granular Systems From Data Streams. IEEE Transactions on Fuzzy Systems, 28(3), 583–596. doi:10.1109/TFUZZ.2019.2911493
https://ieeexplore.ieee.org/document/8691724
[82] Leite, D., Škrjanc, I., & Gomide, F. (2020). An overview on evolving systems and learning from stream data. Evolving Systems, 11, 181-198. doi:10.1007/s12530-020-09334-5
https://link.springer.com/article/10.1007/s12530-020-09334-5
[81] Decker, L., Leite, D., Giommi, L., & Bonacorsi, D. (2020). Real-Time Anomaly Detection in Data Centers for Log-based Predictive Maintenance using an Evolving Fuzzy-Rule-Based Approach. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, UK, 8p. doi:10.1109/FUZZ48607.2020.9177762
https://ieeexplore.ieee.org/document/9177762
[80] Silva, S., Costa, P., Santana, M., & Leite, D. (2020). Evolving neuro-fuzzy network for real-time high impedance fault detection and classification. Neural Computing and Applications, 32, 7597-7610. doi:10.1007/s00521-018-3789-2
https://link.springer.com/article/10.1007/s00521-018-3789-2
[79] Leite, D., Decker, L., Santana, M., & Souza, P. (2020). EGFC: Evolving Gaussian Fuzzy Classifier from Never-Ending Semi-Supervised Data Streams – With Application to Power Quality Disturbance Detection and Classification. 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, UK, 9p. doi:10.1109/fuzz48607.2020.9177847
https://ieeexplore.ieee.org/document/9177847
[78] Decker, L., Leite, D., Viola, F., & Bonacorsi, D. (2020). Comparison of Evolving Granular Classifiers applied to Anomaly Detection for Predictive Maintenance in Computing Centers. IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), Bari, Italy, 8p. doi:10.1109/EAIS48028.2020.9122779
https://ieeexplore.ieee.org/abstract/document/9122779
[77] Aguiar, C., & Leite, D. (2020). Unsupervised Fuzzy eIX: Evolving Internal-eXternal Fuzzy Clustering. IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), Bari, Italy, 8p. doi:10.1109/EAIS48028.2020.9122774
https://ieeexplore.ieee.org/document/9122774
[76] Frigeri Jr., V., Farah, P., Medeiros, R., & Leite, D. (2020). Aprendizado Incremental Online para Classificação Fuzzy de Emoções em Jogos a partir de Fluxos de Dados EEG. Congresso Brasileiro de Automática (CBA), Porto Alegre, 8p. doi:10.48011/asba.v2i1.1014
https://www.sba.org.br/open_journal_systems/index.php/cba/article/view/1014
[75] Mota, V., & Leite, D. (2020). Sistema de Inferência Neuro-Fuzzy para Análise Microbiológica de Processos de Compostagem. Congresso Brasileiro de Automática (CBA), Porto Alegre, 6p. doi:10.48011/asba.v2i1.1435
https://www.sba.org.br/open_journal_systems/index.php/cba/article/view/1435
[74] Decker, L, & Leite, D. (2020). Detecção de Anomalias em Logs para Manutenção Preditiva baseada em Sistema Fuzzy Evolutivo Fracamente Supervisionado. Congresso Brasileiro de Automática (CBA), Porto Alegre, 8p. doi:10.48011/asba.v2i1.1539
https://www.sba.org.br/open_journal_systems/index.php/cba/article/view/1539
[73] Fortunato, D., Santana, M., Gomes, J., & Leite, D. (2020). Modelagem Granular Neuro-Fuzzy Evolutiva para Classificação de Distúrbios em Sistemas de Distribuição de Potência. Congresso Brasileiro de Automática (CBA), Porto Alegre, 7p. doi: 10.48011/asba.v2i1.1666
[72] Santana, M., & Leite, D. (2020). Aprendizado de Máquina Fuzzy Incremental para Classificação de Faltas em Sistemas de Potência. Abakos, Belo Horizonte, 8(2), 3-28. ISSN: 2316-9451. doi:10.5752/P.2316-9451.2020v8n2p03-28
http://periodicos.pucminas.br/index.php/abakos/article/view/18343
[71] Mota, V. C., Andrade, E. T., & Leite, D. F. (2020) Use of Compost Bedded Pack Barn in Maize Fertilization for Silage. Revista do Agronegócio e Meio Ambiente, Maringá, 13(4), 1571-1588. doi:10.17765/2176-9168.2020v13n4p1571-1588
https://www.proquest.com/docview/2451877070/fulltextPDF/E0AACC79C15E40E1PQ/1
[70] Mota, V. C., Andrade, E. T., & Leite, D. F. (2020). Sistema de Confinamento Compost Barn: Interações entre Índices de Conforto, Características Fisiológicas, Escore de Higiene e Claudicação. Arquivos de Ciências Veterinárias e Zoologia da UNIPAR, Umuarama, 23(1), e2308. doi:10.25110/arqvet.v23i1cont.2020.6969
https://revistas.unipar.br/index.php/veterinaria/article/view/6969
ArXiv Preprints 2020
[69] Decker, L., Leite, D., Giommi, L., & Bonacorsi, D. (2020). Real-Time Anomaly Detection in Data Centers for Log-based Predictive Maintenance using an Evolving Fuzzy-Rule-Based Approach. ArXiv Preprint. doi: arXiv:2004.13527
https://arxiv.org/abs/2004.13527
[68] Leite, D., Decker, L., Santana, M., & Souza, P. (2020). EGFC: Evolving Gaussian Fuzzy Classifier from Never-Ending Semi-Supervised Data Streams – With Application to Power Quality Disturbance Detection and Classification. ArXiv Preprint. doi: arXiv:2004.09986
https://arxiv.org/abs/2004.09986
[67] Decker, L., Leite, D., Viola, F., & Bonacorsi, D. (2020). Comparison of Evolving Granular Classifiers applied to Anomaly Detection for Predictive Maintenance in Computing Centers. ArXiv Preprint. doi: arXiv:2005.04156
https://arxiv.org/abs/2005.04156
[66] Aguiar, C., & Leite, D. (2020). Unsupervised Fuzzy eIX: Evolving Internal-eXternal Fuzzy Clustering. ArXiv Preprint. doi: arXiv:2003.12381
https://arxiv.org/abs/2003.12381
2019
[65] Škrjanc, I., Iglesias, J., Sanchis, A., Leite, D., Lughofer, E., & Gomide, F. (2019). Evolving Fuzzy and Neuro-Fuzzy Approaches in Clustering, Regression, Identification, and Classification: A Survey. Information Sciences, 490, 344-368. doi:10.1016/j.ins.2019.03.060
https://www.sciencedirect.com/science/article/abs/pii/S0020025519302713
[64] Leite, D., & Škrjanc, I. (2019). Ensemble of evolving optimal granular experts, OWA aggregation, and time series prediction. Information Sciences, 504, 95–112. doi:10.1016/j.ins.2019.07.053
https://www.sciencedirect.com/science/article/abs/pii/S0020025519306590
[63] Garcia, C., Leite, D., & Skrjanc, I. (2019). Incremental Missing-Data Imputation for Evolving Fuzzy Granular Prediction. IEEE Transactions on Fuzzy Systems, 28(10), 2348–2362. doi:10.1109/tfuzz.2019.2935688
https://ieeexplore.ieee.org/document/8801860
[62] Leite, D. (2019). Comparison of Genetic and Incremental Learning Methods for Neural Network-Based Electrical Machine Fault Detection. In: Predictive Maintenance in Dynamic Systems (Lughofer, E., Sayed-Mouchaweh, M. (eds)), Springer, Cham, 231–268. doi:10.1007/978-3-030-05645-2_8
https://link.springer.com/chapter/10.1007/978-3-030-05645-2_8
[61] Garcia, C., Esmin, A., Leite, D., & Škrjanc, I. (2019). Evolvable fuzzy systems from data streams with missing values: With application to temporal pattern recognition and cryptocurrency prediction. Pattern Recognition Letters, 128, 278-282. doi:10.1016/j.patrec.2019.09.012
https://www.sciencedirect.com/science/article/abs/pii/S0167865518305191
[60] Leite, D., Aguiar, C., Pereira, D., Souza, G., & Skrjanc, I. (2019). Nonlinear Fuzzy State-Space Modeling and LMI Fuzzy Control of Overhead Cranes. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, 6p. doi:10.1109/fuzz-ieee.2019.8858968
https://ieieeexplore.ieee.org/document/8858968
[59] Leite, D., Gomide, F., & Skrjanc, I. (2019). Multiobjective Optimization of Fuzzy Autonomous Evolving Fuzzy Granular Models. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, US, 1–7. doi:10.1109/FUZZ-IEEE.2019.8858964
https://ieeexplore.ieee.org/document/8858964
[58] Soares, E., Garcia, C., Poucas, R., Camargo, H., & Leite, D. (2019). Evolving Fuzzy Set-based and Cloud-based Unsupervised Classifiers for Spam Detection. IEEE Latin America Transactions, 17(09), 1449–1457. doi:10.1109/tla.2019.8931138
https://ieeexplore.ieee.org/document/8931138
[57] Škrjanc, I., Blazic, S., Andonovski, G., Iglesias, J. A., Sanchis, A., & Leite, D. (2019). Incremental Clustering based on Decomposed Cauchy-like Density for Imbalanced Data Classification from Data Stream. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, US, 1108-1113.
[56] Mota, V. C., Andrade, E. T., Pinto, S. M., Abreu, L. R. de, & Leite, D. F. (2019). Utilization of bedded cattle confinement for organic manure of maize crop. Revista Brasileira de Engenharia Agrícola e Ambiental, 23(8), 620–624. doi:10.1590/1807-1929/agriambi.v23n8p620-624
https://www.scielo.br/j/rbeaa/a/jrgGXqYvg4k8HKK4NV7RQZv/?lang=en
[55] Aguiar, C. C. & Leite, D. (2019). Controle Fuzzy PDC baseado em Inequações Matriciais Lineares de um Sistema de Guindaste Overhead. 14º Simpósio Brasileiro de Automação Inteligente, Ouro Preto, 8p. doi:10.17648/sbai-2019-111264
[54] Soares, E., Garcia, C. M., Pouças, R., Lamounier, S. M. D., & Leite, D. (2019). Classificadores Não-Supervisionados baseados em Conjuntos Fuzzy e Nuvens de Dados para Detecção de Spam. 14º Simpósio Brasileiro de Automação Inteligente, Ouro Preto, 8p. doi:10.17648/sbai-2019-111345
[53] Gonçalves, D. & Leite, D. (2019). Autonomia e Otimização Multiobjetivo de Sistemas Granulares Fuzzy Evolutivos: Uma Aplicação em Análise EEG. 14º Simpósio Brasileiro de Automação Inteligente, Ouro Preto, 8p. doi:10.17648/sbai-2019-111310
[52] Barbosa, B. S., Leite, D., Costa Jr., P., & Batalha, R. M. (2019). Sensores Virtuais para Detecção de Descargas Parciais de Transformadores de Potência. 14º Simpósio Brasileiro de Automação Inteligente, Ouro Preto, 6p. doi:10.17648/sbai-2019-111186
[51] Tavares, J. P., Lima, D. A., & Leite, D. (2019). Controle Granular Fuzzy Evolutivo Aplicado à Navegação de Veículos Inteligentes. 14º Simpósio Brasileiro de Automação Inteligente, Ouro Preto, 6p. doi:10.17648/sbai-2019-111382
[50] Gonçalves, D., Garcia, C. M., Lacerda, W. S., & Leite, D. (2019). Classificação do Estado dos Olhos via Dados EEG e Redes Neurais Feed-Forward, Recorrente e Evolutiva. 14º Simpósio Brasileiro de Automação Inteligente, Ouro Preto, 6p. doi:10.17648/sbai-2019-111309
[49] Guimarães, R. A., Ferreira, S. C., Pacheco, V. M., Pedroso, J. P., Ribeiro, J. E., Viana, O. S., & Leite, D. F. (2019). Controle Preditivo baseado em Modelo para Conversores Formadores de Rede com Operação Ilhada. 14º Simpósio Brasileiro de Automação Inteligente, Ouro Preto, 6p. doi:10.17648/sbai-2019-111536
[48] Mota, V. C., Andrade, E. T. de, & Leite, D. F. (2019). Bed Temperature in Compost Barns Turned with Rotary Hoe and Offset Disc Harrow. Engenharia Agrícola, 39(3), 280–287. doi:10.1590/1809-4430-eng.agric.v39n3p280-287/2019
https://www.scielo.br/j/eagri/a/SNj4PrFJG5HBjwfrsc3X6pN/?lang=en
[47] Mota, V. C., Andrade, E. T., & Leite, D. F. (2019). Caracterização da Variabilidade Espacial dos Índices de Conforto Animal em Sistemas de Confinamento Compost Barn. PUBVET, 13(2), 14p. doi:10.31533/pubvet.v13n3a276.1-14
https://doi.org/10.31533/pubvet.v13n3a276.1-14
2018
[46] Silva, S., Costa, P., Gouvea, M., Lacerda, A., Alves, F., & Leite, D. (2018). High impedance fault detection in power distribution systems using wavelet transform and evolving neural network. Electric Power Systems Research, 154, 474–483. doi:10.1016/j.epsr.2017.08.039
https://www.sciencedirect.com/science/article/abs/pii/S0378779617303644
[45] Soares, E., Costa, P., Costa, B., & Leite, D. (2018). Ensemble of evolving data clouds and fuzzy models for weather time series prediction. Applied Soft Computing, 64, 445–453. doi:10.1016/j.asoc.2017.12.032
https://www.sciencedirect.com/science/article/abs/pii/S1568494617307573
[44] Mota, V. C., Damasceno, F. A., & Leite, D. F. (2018). Fuzzy clustering and fuzzy validity measures for knowledge discovery and decision making in agricultural engineering. Computers and Electronics in Agriculture, 150, 118–124. doi:10.1016/j.compag.2018.04.011
https://doi.org/10.1016/j.compag.2018.04.011
[43] Soares, E. A., Camargo, H. A., Camargo, S. J., & Leite, D. F. (2018). Incremental Gaussian Granular Fuzzy Modeling Applied to Hurricane Track Forecasting. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Rio de Janeiro, 8p. doi:10.1109/fuzz-ieee.2018.8491587
https://ieeexplore.ieee.org/document/8491587
[42] Lucas, F., Costa, P., Batalha, R., & Leite, D. (2018). High Impedance Fault Detection in Time-Varying Distributed Generation Systems Using Adaptive Neural Networks. 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, 8p. doi:10.1109/ijcnn.2018.8489453
https://ieeexplore.ieee.org/document/8489453
[41] Ribeiro, T. J., Borges, M. T., Cardoso, R. A., Coelho, R. R., Costa, S., & Leite, D. (2018). Classificação Fuzzy de Padrões Não-Motores e Indicação da Severidade da Doença de Parkinson. Congresso Brasileiro de Automática (CBA), João Pessoa, 7p.
[40] Silva, M. J., Rufino Jr., C. A., Ferreira, S. C., Ferreira, D. D., Barbosa, L. M., & Leite, D. F. (2018). Aplicação de Filtros Adaptativos para a Estimação da Frequência e da Amplitude de Inter-Harmônicos causados por Flutuação de Tensão. Congresso Brasileiro de Automática (CBA), João Pessoa, 8p.
[39] Souza, C. A. T., Ferreira, S. C., & Leite, D. F. (2018). Controle Intervalar de Sistemas Lineares com Incerteza Paramétrica Não-Estruturada. Congresso Brasileiro de Automática (CBA), João Pessoa, 8p.
[38] Tavares, J. P., Lima, D. A., & Leite, D. F. (2018). Modelagem Granular Fuzzy Evolutiva para Predição da Posição Ângular do Volante de um Automável. V Congresso Brasileiro de Sistemas Fuzzy (CBSF), Fortaleza, 12p.
2017
[37] Soares, E., Mota, V., Poucas, R., & Leite, D. (2017). Cloud-based evolving intelligent method for weather time series prediction. 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, 6p. doi:10.1109/fuzz-ieee.2017.8015532
https://ieeexplore.ieee.org/document/8015532
[36] Mota, V. C., Damasceno, F. A., Soares, E. A., & Leite, D. F. (2017). Fuzzy clustering methods applied to the evaluation of compost bedded pack barns. 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, 6p. doi:10.1109/fuzz-ieee.2017.8015435
https://ieeexplore.ieee.org/document/8015435
[35] Mota, V., Soares, E., & Leite, D. (2017). Modelagem Fuzzy Incremental para Previsão Climática. XLVI Congresso Brasileiro de Engenharia Agrícola (CONBEA), Maceió, BR, 10p.
[34] Mota, V., Campos, A., Damasceno, F., Soares, E., & Leite, D. (2017). Métodos de Agrupamento Fuzzy Aplicados à Sistemas de Galpão de Compostagem. XLVI Congresso Brasileiro de Engenharia Agrícola (CONBEA), Maceió, BR, 10p.
2016
[33] Leite, D., Santana, M., Borges, A., & Gomide, F. (2016). Fuzzy Granular Neural Network for incremental modeling of nonlinear chaotic systems. 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Vancouver, 64-71. doi:10.1109/fuzz-ieee.2016.7737669
https://ieeexplore.ieee.org/document/7737669
[32] Leite, D., Costa Jr., P, & Gomide, F. (2016). A Review on Evolving Interval and Fuzzy Granular Systems. Learning and Nonlinear Models - Journal of the Brazilian Society on Computational Intelligence (SBIC), 14(2), 36-54. doi:10.21528/LNLM-vol14-no2-art3
http://abricom.org.br/wp-content/uploads/sites/4/2017/04/vol14-no2-art3.pdf
2015
[31] Leite, D., Palhares, R. M., Campos, V. C. S., & Gomide, F. (2015). Evolving Granular Fuzzy Model-Based Control of Nonlinear Dynamic Systems. IEEE Transactions on Fuzzy Systems, 23(4), 923–938. doi:10.1109/tfuzz.2014.2333774
https://ieeexplore.ieee.org/document/6846287
[30] Bueno, L., Costa, P., Mendes, I., Cruz, E., & Leite, D. (2015). Evolving ensemble of fuzzy models for multivariate time series prediction. 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Istanbul, 6p. doi:10.1109/fuzz-ieee.2015.7338002
https://ieeexplore.ieee.org/document/7338002
[29] Leite, D., & Gomide, F. (2015). Incremental Granular Fuzzy Modeling using Imprecise Data Streams. In: Fifty Years of Fuzzy Logic and its Applications. Studies in Fuzziness and Soft Computing (Dan Tamir, Naphtali Rishe, Abraham Kandel (eds)), 326, 107-124. Springer, Cham. doi:10.1007/978-3-319-19683-1_7
2014
[28] Leite, D., Caminhas, W., Lemos, A., Palhares, R., & Gomide, F. (2014). Parameter estimation of dynamic fuzzy models from uncertain data streams. 2014 IEEE Conference on Norbert Wiener in the 21st Century (21CW), Boston, 7p. doi:10.1109/norbert.2014.6893892
https://ieeexplore.ieee.org/document/6893892
[27] Mendes, I., Costa, P., Bueno, L., & Leite, D. (2014). Redes Neuro-Fuzzy Evolutivas Aplicadas ao Gerenciamento de Motores de Combustão Interna. Congresso Brasileiro de Sistemas Fuzzy (CBSF), João Pessoa, BR, 12p.
[26] Bueno, L., Costa, P., Cruz, E., Mendes, I., & Leite, D. (2014). Agrupamento Evolutivo Aplicado ao Reconhecimento de Padrões em Dados Médicos. XX Congresso Brasileiro de Automática, Belo Horizonte, 1240-1245.
http://www.swge.inf.br/cba2014/anais/PDF/1569932959.pdf
[25] Mendes, I., Costa, P., Bueno, L., & Leite, D. (2014). Modelagem de Motores de Combustão via Métodos Evolutivos Embarcados. XX Congresso Brasileiro de Automática, Belo Horizonte, 4233-4239.
2013
[24] Leite, D., Costa, P., & Gomide, F. (2013). Evolving granular neural networks from fuzzy data streams. Neural Networks, 38, 1–16. doi:10.1016/j.neunet.2012.10.006
https://www.sciencedirect.com/science/article/abs/pii/S0893608012002791
2012
[23] Leite, D., Ballini, R., Costa, P., & Gomide, F. (2012). Evolving fuzzy granular modeling from nonstationary fuzzy data streams. Evolving Systems, 3(2), 65–79. doi:10.1007/s12530-012-9050-9
https://link.springer.com/article/10.1007/s12530-012-9050-9
[22] Leite, D. F., Costa, P., & Gomide, F. (2012). Interval Approach for Evolving Granular Systems Modeling. In: Learning in Non-Stationary Environments - Springer, New York, NY, 271-300. doi:10.1007/978-1-4419-8020-5_11
https://link.springer.com/chapter/10.1007/978-1-4419-8020-5_11
[21] Leite, D., Costa, P., & Gomide, F. (2012). Evolving granular neural network for fuzzy time series forecasting. 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, 8p. doi:10.1109/ijcnn.2012.6252382
https://ieeexplore.ieee.org/document/6252382
[20] Leite, D., & Gomide, F. (2012). Evolving Linguistic Fuzzy Models from Data Streams. In: Combining Experimentation and Theory. Studies in Fuzziness and Soft Computing, 271, Springer – Berlin, Heidelberg, 209-223. doi:10.1007/978-3-642-24666-1_15
https://link.springer.com/chapter/10.1007/978-3-642-24666-1_15
[19] Leite, D. Evolving Granular Systems. (2012). PhD Thesis. State University of Campinas (UNICAMP), School of Electrical and Computer Engineering, Campinas, SP, Brazil, 188p.
https://www.researchgate.net/publication/334446175_Evolving_Granular_Systems
[18] Lemos, A., Leite, D., Maciel, L., Ballini, R., Caminhas, W., & Gomide, F. (2012). Evolving fuzzy linear regression tree approach for forecasting sales volume of petroleum products. 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Brisbane, 8p. doi:10.1109/fuzz-ieee.2012.6250809
https://ieeexplore.ieee.org/document/6250809
[17] Mendes, I., Costa, P., Bergo, L., & Leite, D. (2012). Redes Neuro-Fuzzy Evolutivas Embarcadas em Sistemas Microcontrolados. Congresso Brasileiro de Sistemas Fuzzy (CBSF), Natal, 630–644.
http://www.dimap.ufrn.br/~cbsf/pub/anais/2012/10000630.pdf
2011
[16] Leite, D., Gomide, F., Ballini, R., & Costa, P. (2011). Fuzzy granular evolving modeling for time series prediction. 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), Taipei, 2794-2801. doi:10.1109/fuzzy.2011.6007452
https://ieeexplore.ieee.org/document/6007452
[15] Leite, D. F., Ballini, R., Costa, P., & Gomide, F. (2011). Modelagem Evolutiva Granular Fuzzy. X Simpósio Brasileiro de Automação Inteligente (SBAI), São João Del Rey, 81-86.
https://fei.edu.br/sbai/SBAI2011/86449.pdf
2010
[14] Leite, D., Costa, P., & Gomide, F. (2010). Evolving granular neural network for semi-supervised data stream classification. The 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, 8p. doi:10.1109/ijcnn.2010.5596303
https://ieeexplore.ieee.org/document/5596303
[13] Leite, D., Costa Jr., P., & Gomide, F. (2010). Granular Approach for Evolving System Modeling. In: Computational Intelligence for Knowledge-based Systems Design (IPMU). Lecture Notes in Computer Science, 6178. Springer, Berlin, Heidelberg. doi:10.1007/978-3-642-14049-5_35
https://link.springer.com/chapter/10.1007/978-3-642-14049-5_35
[12] Leite, D. F., Costa, P., & Gomide, F. (2010). Redes Neurais Granulares para Aprendizagem Incremental Semi-Supervisionada. XVIII Congresso Brasileiro de Automática (CBA), Bonito, MS, 2592–2599.
[11] Leite, D., Nascimento, L., Barbosa, A., Costa Jr., P., Ferreira, D. & Gomide, F. (2010) Sistema de Diagnóstico de Falta em Transformadores baseado em Inteligência Computacional Evolutiva. IV International Workshop on Power Transformers (WORKSPOT), Foz do Iguaçú, BR, 8p.
2009
[10] Leite, D. F., Hell, M. B., Costa, P., & Gomide, F. (2009). Real-time fault diagnosis of nonlinear systems. Nonlinear Analysis: Theory, Methods & Applications, 71(12), e2665–e2673. doi:10.1016/j.na.2009.06.037
https://www.sciencedirect.com/science/article/abs/pii/S0362546X09007809
[9] Leite, D. F., Costa, P., & Gomide, F. (2009). Evolving granular classification neural networks. 2009 International Joint Conference on Neural Networks (IJCNN), Atlanta, 1736-1743. doi:10.1109/ijcnn.2009.5178895
https://ieeexplore.ieee.org/abstract/document/5178895
[8] Leite, D. F., Costa, P., & Gomide, F. (2009). Interval-based evolving modeling. 2009 IEEE Workshop on Evolving and Self-Developing Intelligent Systems (ESDIS), Nashville, 8p. doi:10.1109/esdis.2009.4938992
https://ieeexplore.ieee.org/document/4938992
[7] Leite, D. F., Bergo Jr., L., Costa Jr., P., & Gomide, F. (2009). Redes Neurais Granulares Evolutivas em Modelagem de Sistemas. IX Congresso Brasileiro de Redes Neurais / Inteligência Computacional (IX CBRN), Ouro Preto, 5p.
https://sbic.org.br/wp-content/uploads/2016/12/059_CBRN2009.pdf
[6] Leite, D. F., Attux, R., VonZuben, F., Costa Jr., P, & Gomide, F. (2009) Evolutionary Neural Network Applied to Induction Motors Stator Fault Detection. IEEE International Electric Machines & Drives Conference (IEMDC), Miami, 1721-1728.
[5] Leite, D. F., Costa Jr., P., & Gomide, F. (2009). Redes Neurais Granulares Evolutivas. IX Simpósio Brasileiro de Automação Inteligente (SBAI), Brasilia, DF, 1–6.
[4] Leite, D. F., Costa Jr., P., & Gomide, F. (2009). Sistemas Conexionistas Evolutivos. IX Simpósio Brasileiro de Automação Inteligente (SBAI), Brasilia, DF, 7–12.
2007
[3] Leite, D. F., Hell, M. B., Diez, P. H., Gariglio, B. S. L., Nascimento, L. O., & Costa, P. (2007). Real-Time Model-Based Fault Detection and Diagnosis for Alternators and Induction Motors. 2007 IEEE International Electric Machines & Drives Conference (IEMDC), Antalya, 202-207. doi:10.1109/iemdc.2007.383577
https://ieeexplore.ieee.org/document/4270639
[2] Leite, D. F., Araujo, M. V., Secco, L., & Costa, P. (2007). Induction Motors Modeling and Fuzzy Logic Based Turn-To-Turn Fault Detection and Localization. 2007 International Conference on Power Engineering, Energy and Electrical Drives (POWERENG), Setubal, 90-95. doi:10.1109/powereng.2007.4380217
https://ieeexplore.ieee.org/abstract/document/4380217
[1] Leite, D. F. (2007). Sistema de Diagnóstico de Faltas em Máquinas Elétricas de Corrente Alternada. Dissertação de Mestrado. Pontifícia Universidade Católica de Minas Gerais – PPGEE.
http://www.biblioteca.pucminas.br/teses/EngEletrica_LeiteDF_1.pdf