| Martins, V. E., Cano, A., & Junior, S. B. (2023). Meta-learning for dynamic tuning of Active Learning on Stream Classification. Pattern Recognition, 109359. |
| Tavares, G. M., Barbon Junior, S., Damiani, E., & Ceravolo, P. (2022, November). Selecting Optimal Trace Clustering Pipelines with Meta-learning. In Intelligent Systems: 11th Brazilian Conference, BRACIS 2022, Campinas, Brazil, November 28–December 1, 2022, Proceedings, Part I (pp. 150-164). Cham: Springer International Publishing. |
| Tavares, G. M., Junior, S. B., & Damiani, E. (2022, September). Automating Process Discovery Through Meta-learning. In Cooperative Information Systems: 28th International Conference, CoopIS 2022, Bozen-Bolzano, Italy, October 4–7, 2022, Proceedings (pp. 205-222). Cham: Springer International Publishing. |
| Oyamada, R. S., Shimomura, L. C., Barbon Jr, S., & Kaster, D. S. (2023). A meta-learning configuration framework for graph-based similarity search indexes. Information Systems, 112, 102123. |
| Lopes, J. F., da Costa, V. G. T., Barbin, D. F., Cruz-Tirado, L. J. P., Baeten, V., & Barbon Junior, S. (2022). Deep computer vision system for cocoa classification. Multimedia Tools and Applications, 81(28), 41059-41077. |
| Alberghini, G., Junior, S. B., & Cano, A. (2022). Adaptive ensemble of self-adjusting nearest neighbor subspaces for multi-label drifting data streams. Neurocomputing, 481, 228-248. |
| Nakagawa, F. H., Junior, S. B., & Zarpelao, B. B. (2021, November). Attack Detection in Smart Home IoT Networks using CluStream and Page-Hinkley Test. In 2021 IEEE Latin-American Conference on Communications (LATINCOM) (pp. 1-6). IEEE. |
| Barbon Junior, S., Pinto, A., Barroso, J. V., Caetano, F. G., Moura, F. A., Cunha, S. A., & Torres, R. D. S. (2022). Sport action mining: Dribbling recognition in soccer. Multimedia Tools and Applications, 81(3), 4341-4364. |
| Caetano, F. G., Barbon Junior, S., Torres, R. D. S., Cunha, S. A., Ruffino, P. R. C., Martins, L. E. B., & Moura, F. A. (2021). Football player dominant region determined by a novel model based on instantaneous kinematics variables. Scientific Reports, 11(1), 18209. |
| Scaranti, G. F., Carvalho, L. F., Junior, S. B., Lloret, J., & Proença Jr, M. L. (2022). Unsupervised online anomaly detection in Software Defined Network environments. Expert Systems with Applications, 191, 116225. |
| Aguiar, G. J., Santana, E. J., de Carvalho, A. C., & Junior, S. B. (2022). Using meta-learning for multi-target regression. Information Sciences, 584, 665-684. |
| Silva, R. P., Zarpelão, B. B., Cano, A., & Junior, S. B. (2021). Time series segmentation based on stationarity analysis to improve new samples prediction. Sensors, 21(21), 7333. |
| Santana, E. J., Silva, R. P., Zarpelão, B. B., & Barbon Junior, S. (2021). Detecting and mitigating adversarial examples in regression tasks: a photovoltaic power generation forecasting case study. Information, 12(10), 394. |
| Abonizio, H. Q., Paraiso, E. C., & Barbon, S. (2021). Toward text data augmentation for sentiment analysis. IEEE Transactions on Artificial Intelligence, 3(5), 657-668. |
| Barbon Jr, S., Ceravolo, P., Damiani, E., & Tavares, G. M. (2021). Selecting optimal trace clustering pipelines with automl. arXiv preprint arXiv:2109.00635. |
| Tavares, G. M., & Junior, S. B. (2021, July). Process Mining Encoding via Meta-learning for an Enhanced Anomaly Detection. In New Trends in Database and Information Systems: ADBIS 2021 Short Papers, Doctoral Consortium and Workshops: DOING, SIMPDA, MADEISD, MegaData, CAoNS, Tartu, Estonia, August 24-26, 2021, Proceedings (pp. 157-168). Cham: Springer International Publishing. |
| Peres, L. M., Junior, S. B., Lopes, J. F., Fuzyi, E. M., Barbon, A. P., Armangue, J. G., & Bridi, A. M. (2021). Meta-recommendation of pork technological quality standards. Biosystems Engineering, 210, 13-19. |
| Azzini, A., Barbon Jr, S., Bellandi, V., Catarci, T., Ceravolo, P., Cudré-Mauroux, P., … & Wrembel, R. (2021). Advances in data management in the big data era. In Advancing Research in Information and Communication Technology: IFIP’s Exciting First 60+ Years, Views from the Technical Committees and Working Groups (pp. 99-126). Cham: Springer International Publishing. |
| Vertuam Neto, R., Tavares, G., Ceravolo, P., & Barbon, S. (2021, June). On the use of online clustering for anomaly detection in trace streams. In XVII Brazilian Symposium on Information Systems (pp. 1-8). |
| Barbon Jr, S., Ceravolo, P., Damiani, E., & Tavares, G. M. (2021). Using Meta-learning to recommend process discovery methods. arXiv preprint arXiv:2103.12874. |
| Zarpelão, B. B., Junior, S. B., Badaró, A. T., & Barbin, D. F. (2021). On the use of blockchain for agrifood traceability. In Food Authentication and Traceability (pp. 279-302). Academic Press. |
| Barbon Junior, S., Ceravolo, P., Damiani, E., & Marques Tavares, G. (2021). Evaluating trace encoding methods in process mining. In From Data to Models and Back: 9th International Symposium, DataMod 2020, Virtual Event, October 20, 2020, Revised Selected Papers 9 (pp. 174-189). Springer International Publishing. |
| Oliveira, M. M., Cerqueira, B. V., Barbon Jr, S., & Barbin, D. F. (2021). Classification of fermented cocoa beans (cut test) using computer vision. Journal of Food Composition and Analysis, 97, 103771. |
| Mastelini, S. M., Santana, E. J., Cerri, R., & Barbon Jr, S. (2020). DSTARS: A multi-target deep structure for tracking asynchronous regressor stacking. Applied Soft Computing, 91, 106215. |
| Junior, S. B., Ceravolo, P., Damiani, E., Omori, N. J., & Tavares, G. M. (2020, October). Anomaly detection on event logs with a scarcity of labels. In 2020 2nd International Conference on Process Mining (ICPM) (pp. 161-168). IEEE. |
| Martins, V. E., da Costa, V. G. T., & Barbon Junior, S. (2020, October). Active learning embedded in incremental decision trees. In Intelligent Systems: 9th Brazilian Conference, BRACIS 2020, Rio Grande, Brazil, October 20–23, 2020, Proceedings, Part II (pp. 367-381). Cham: Springer International Publishing. |
| Santana, E. J., Silva, R. P., Zarpelão, B. B., & Barbon Junior, S. (2020, October). Photovoltaic generation forecast: model training and adversarial attack aspects. In Intelligent Systems: 9th Brazilian Conference, BRACIS 2020, Rio Grande, Brazil, October 20–23, 2020, Proceedings, Part II (pp. 634-649). Cham: Springer International Publishing. |
| Queiroz Abonizio, H., & Barbon Junior, S. (2020, October). Pre-trained data augmentation for text classification. In Intelligent Systems: 9th Brazilian Conference, BRACIS 2020, Rio Grande, Brazil, October 20–23, 2020, Proceedings, Part I (pp. 551-565). Cham: Springer International Publishing. |
| Junior, S. B., Santana, E. J., Badaró, A. T., Borrás, N. A., & Barbin, D. F. (2020). Advantages of Multi-Target Modelling for Spectral Regression. Spectroscopic Techniques & Artificial Intelligence for Food and Beverage Analysis, 95-121. |
| Oyamada, R. S., Shimomura, L. C., Junior, S. B., & Kaster, D. S. (2020, August). Towards proximity graph auto-configuration: An approach based on meta-learning. In Advances in Databases and Information Systems: 24th European Conference, ADBIS 2020, Lyon, France, August 25–27, 2020, Proceedings (pp. 93-107). Cham: Springer International Publishing. |
| Tavares, G. M., & Barbon, S. (2020). Analysis of language inspired trace representation for anomaly detection. In ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium: International Workshops: DOING, MADEISD, SKG, BBIGAP, SIMPDA, AIMinScience 2020 and Doctoral Consortium, Lyon, France, August 25–27, 2020, Proceedings 24 (pp. 296-308). Springer International Publishing. |
| Mantovani, R. G., Rossi, A. L. D., Alcobaça, E., Gertrudes, J. C., Junior, S. B., & de Carvalho, A. C. P. D. L. F. (2020). Rethinking default values: a low cost and efficient strategy to define hyperparameters. arXiv preprint arXiv:2008.00025. |
| Ceravolo, P., Tavares, G. M., Junior, S. B., & Damiani, E. (2020). Evaluation goals for online process mining: a concept drift perspective. IEEE Transactions on Services Computing, 15(4), 2473-2489. |
| Jashchenko Omori, N., Marques Tavares, G., Ceravolo, P., & Barbon, S. (2020). Comparing concept drift detection with process mining software. ISYS, 13(4), 101-125. |
| de Morais, J. I., Abonizio, H. Q., Tavares, G. M., da Fonseca, A. A., & Barbon Jr, S. (2020). A multi-label classification system to distinguish among fake, satirical, objective and legitimate news in brazilian portuguese. iSys-Brazilian Journal of Information Systems, 13(4), 126-149. |
| Leão, A. L. F., Abonizio, H. Q., Júnior, S. B., & Kanashiro, M. (2020). Identificação de composições da paisagem urbana: uma abordagem de deep learning. Revista de Morfologia Urbana, 8(1), e00140-e00140. |
| Zarpelão, B. B., Barbon, S., Acarali, D., & Rajarajan, M. (2020). How Machine Learning Can Support Cyberattack Detection in Smart Grids. Artificial Intelligence Techniques for a Scalable Energy Transition: Advanced Methods, Digital Technologies, Decision Support Tools, and Applications, 225-258. |
| Scaranti, G. F., Carvalho, L. F., Barbon, S., & Proença, M. L. (2020). Artificial immune systems and fuzzy logic to detect flooding attacks in software-defined networks. IEEE Access, 8, 100172-100184. |
| Abonizio, Hugo Queiroz, et al. “Language-independent fake news detection: English, Portuguese, and Spanish mutual features.” Future Internet 12.5 (2020): 87. |
| Lopes, J, Santana, E, Costa, V, Zarpelão, B, Barbon, S. “Evaluating the Four-way Performance Trade-off for Data Stream Classification in Edge Computing”. IEEE Transactions on Network and Service Management 2020. |
| Campos, G, Seixas Jr, J, Barbon, A, Felinto, A, Bridi, A, Barbon Jr, S. “Robust computer vision system for marbling meat segmentation”. ELCVIA Electronic Letters on Computer Vision and Image Analysis 2020; 19(1):15–27. |
| Mastelini, S, Santana, E, Cerri, R, Barbon Jr, S. “DSTARS: A multi-target deep structure for tracking asynchronous regressor stacking”. Applied Soft Computing 2020:106215. |
| Santana, E, Santos, F, Mastelini, S, Melquiades, F, Barbon Jr, S. “Improved prediction of soil properties with Multi-target Stacked Generalisation on EDXRF spectra”. arXiv preprint arXiv:2002.04312 2020. |
| Lopes, J, Barbon, A, Orlandi, G, Calvini, R, Fiego, D, Ulrici, A, Barbon Jr, S. “Dual Stage Image Analysis for a complex pattern classification task: Ham veining defect detection”. Biosystems Engineering 2020; 191:129–144. |
| Martiello Mastelini, S, Barbon Jr, S, Carvalho, A. “Online Multi-target regression trees with stacked leaf models”. arXiv preprint arXiv:1903.12483 2019. |
| Silva, J, Santana, E, Mastelini, S, Barbon Jr, SPredi\cc\~ao de Portf\’olio de A\cc\~oes por Suporte \`a Decis\~ao Multi-Target. In Anais do XIV Simp\’osio Brasileiro de Sistemas de Informa\cc\~ao 2018 (pp. 269–262). |
| Tavares, G, Costa, V, Martins, V, Ceravolo, P, Barbon Jr, SDetec\cc\~ao de anomalia no processo de neg\’ocios com base na minera\cc\~ao do fluxo de dados. In Anais do XIV Simp\’osio Brasileiro de Sistemas de Informa\cc\~ao 2018 (pp. 127–120). |
| Douglas, F, Saulo, M, Gabriel, F, Ana Paula, A, others. “Digital Image Analyses As An Alternative Tool For Chicken Quality Assessment”. Biosystems Engineering 2016. |
| Fantinato, P, Guido, R, Chen, SH, Santos, B, Vieira, L, J\’unior, S, Rodrigues, L, Sanchez, FJoao Paulo Lemos Escola, Leonardo Mendes Souza, Carlos Dias Maciel, Paulo Rogério Scalassara, José Carlos Pereira, A Fractal-Based Approach for Speech Segmentation. In Proceedings of the 2008 Tenth IEEE International Symposium on Multimedia 2008 (pp. 551–555). |
| Aguiar, G, Mantovani, R, Mastelini, S, Carvalho, A, Campos, G, Junior, S. “A meta-learning approach for selecting image segmentation algorithm”. Pattern Recognition Letters 2019; 128:480–487. |
| Tavares, G, Ceravolo, P, Da Costa, V, Damiani, E, Junior, SOverlapping Analytic Stages in Online Process Mining. In 2019 IEEE International Conference on Services Computing (SCC) 2019 (pp. 167–175). |
| Fonseca, E, Guido, R, Junior, S, Dezani, H, Gati, R, Pereira, D. “Acoustic investigation of speech pathologies based on the discriminative paraconsistent machine (DPM)”. Biomedical Signal Processing and Control 2020; 55:101615. |
| Morais, J, Abonizio, H, Tavares, G, Fonseca, A, Barbon Jr, SDeciding among Fake, Satirical, Objective and Legitimate news: A multi-label classification system. In Proceedings of the XV Brazilian Symposium on Information Systems 2019 (pp. 1–8). |
| Omori, N, Tavares, G, Ceravolo, P, Barbon Jr, SComparing Concept Drift Detection with Process Mining Tools. In Proceedings of the XV Brazilian Symposium on Information Systems 2019 (pp. 1–8). |
| Prece, B, Pacheco, E, Barros, R, Barbon Jr, SImprovements on diagnostic assessment questionnaires of Maturity Level Management with feature selection. In Proceedings of the XV Brazilian Symposium on Information Systems 2019 (pp. 1–8). |
| Aguiar, G, Santana, E, Mastelini, S, Mantovani, R, J\’unior, STowards meta-learning for multi-target regression problems. In 2019 8th Brazilian Conference on Intelligent Systems (BRACIS) 2019 (pp. 377–382). |
| Bezerra, V, Costa, V, Barbon Junior, S, Miani, R, Zarpel\~ao, B. “IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices”. Sensors 2019; 19(14):3188. |
| Costa, V, Mastelini, S, Leon Ferreira, A, Barbon, S, othersOnline Local Boosting: improving performance in online decision trees. In 2019 8th Brazilian Conference on Intelligent Systems (BRACIS) 2019 (pp. 132–137). |
| Tavares, G, Costa, V, Martins, V, Ceravolo, P, Barbon Jr, S. “Leveraging Anomaly Detection in Business Process with Data Stream Mining”. iSys-Revista Brasileira de Sistemas de Informa\cc\~ao 2019; 12(1):54–75. |
| Basantia, N, Nollet, L, Kamruzzaman, M. Hyperspectral Imaging Analysis and Applications for Food Quality. CRC Press; 2018. |
| Costa, V, Santana, E, Lopes, J, Barbon, SEvaluating the Four-Way Performance Trade-Off for Stream Classification. In International Conference on Green, Pervasive, and Cloud Computing 2019 (pp. 3–17). |
| Kato, T, Mastelini, S, Campos, G, Costa Barbon, A, Prudencio, S, Shimokomaki, M, Soares, A, Barbon Jr, S. “White striping degree assessment using computer vision system and consumer acceptance test”. Asian-Australasian Journal of Animal Sciences 2019; 32(7):1015. |
| Junior, S, Mastelini, S, Barbon, A, Barbin, D, Calvini, R, Lopes, J, Ulrici, A. “Multi-target Prediction of wheat flour quality parameters with near infrared spectroscopy”. Information Processing in Agriculture 2019. |
| Lopes, J, Ludwig, L, Barbin, D, Grossmann, M, Barbon, S. “Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble”. Sensors 2019; 19(13):2953. |
| Costa, V, Barbon, S, Miani, R, Rodrigues, J, Zarpel\~ao, B. “Mobile botnets detection based on machine learning over system calls”. International Journal of Security and Networks 2019; 14(2):103–118. |
| Costa, V, Santana, E, Lopes, J, Barbon, SEvaluating the Four-Way Performance Trade-Off for Stream Classification. In International Conference on Green, Pervasive, and Cloud Computing 2019 (pp. 3–17). |
| Nolasco-Perez, I, Rocco, L, Cruz-Tirado, J, Pollonio, M, Barbon, S, Barbon, A, Barbin, D. “Comparison of rapid techniques for classification of ground meat”. Biosystems Engineering 2019; 183:151–159. |
| Campos, G, Mastelini, S, Aguiar, G, Mantovani, R, Melo, L, Barbon, S. “Machine learning hyperparameter selection for Contrast Limited Adaptive Histogram Equalization”. EURASIP Journal on Image and Video Processing 2019; 2019(1):59. |
| Kido, G, Junior, S, Moriguchi, S. Compara\cc\~ao entre TF-IDF e LSI para pesagem de termos em micro-blog. |
| Junior, S, Costa, V, Chen, SH, Guido, RU-Healthcare System for Pre-Diagnosis of Parkinson’s Disease from Voice Signal. In 2018 IEEE International Symposium on Multimedia (ISM) 2018 (pp. 271–274). |
| Costa, V, Mastelini, S, Carvalho, A, Barbon, SMaking Data Stream Classification Tree-Based Ensembles Lighter. In 2018 7th Brazilian Conference on Intelligent Systems (BRACIS) 2018 (pp. 480–485). |
| Mastelini, S, Santana, E, Costa, V, Barbon, SBenchmarking Multi-target Regression Methods. In 2018 7th Brazilian Conference on Intelligent Systems (BRACIS) 2018 (pp. 396–401). |
| Mantovani, R, Horváth, T, Cerri, R, Junior, S, Vanschoren, J, Carvalho, A, Ferreira, L. “An empirical study on hyperparameter tuning of decision trees”. arXiv preprint arXiv:1812.02207 2018. |
| Geronimo, B, Mastelini, S, Carvalho, R, J\’unior, S, Barbin, D, Shimokomaki, M, Ida, E. “Computer vision system and near-infrared spectroscopy for identification and classification of chicken with wooden breast, and physicochemical and technological characterization”. Infrared Physics & Technology 2019; 96:303–310. |
| Kato, T, Mastelini, S, Campos, G, Costa Barbon, A, Prudencio, S, Shimokomaki, M, Soares, A, Barbon, S. “White striping degree assessment using computer vision system and consumer acceptance test”. Asian-Australasian Journal of Animal Sciences 2018; 32(7):1015–1026. |
| Tavares, G, Costa, V, Martins, V, Ceravolo, P, Barbon Jr, SAnomaly detection in business process based on data stream mining. In Proceedings of the XIV Brazilian Symposium on Information Systems 2018 (pp. 1–8). |
| Silva, J, Santana, E, Mastelini, S, Barbon Jr, SStock Portfolio Prediction by Multi-Target Decision Support. In Proceedings of the XIV Brazilian Symposium on Information Systems 2018 (pp. 1–8). |
| Bezerra, V, Costa, V, Martins, R, Junior, S, Miani, R, Zarpelao, BProviding IoT host-based datasets for intrusion detection research∗. In Anais do XVIII Simp\’osio Brasileiro em Seguran\cca da Informa\cc\~ao e de Sistemas Computacionais 2018 (pp. 15–28). |
| Bezerra, V, Costa, V, Junior, S, Miani, R, Zarpelao, BOne-class Classification to Detect Botnets in IoT devices∗. In Anais do XVIII Simp\’osio Brasileiro em Seguran\cca da Informa\cc\~ao e de Sistemas Computacionais 2018 (pp. 43–56). |
| Nakano, F, Mastelini, S, Barbon, S, Cerri, RImproving Hierarchical Classification of Transposable Elements using Deep Neural Networks. In 2018 International Joint Conference on Neural Networks (IJCNN) 2018 (pp. 1–8). |
| Mastelini, S, Sasso, M, Campos, G, Schmiele, M, Clerici, M, Barbin, D, Barbon, S. “Computer vision system for characterization of pasta (noodle) composition”. Journal of Electronic Imaging 2018; 27(5):053021. |
| Almeida, A, Cerri, R, Paraiso, E, Mantovani, R, Junior, S. “Applying multi-label techniques in emotion identification of short texts”. Neurocomputing 2018; 320:35–46. |
| Barbon, S, Costa Barbon, A, Mantovani, R, Barbin, D. “Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification”. Journal of Spectroscopy 2018; 2018. |
| Nolasco Perez, I, Badar\’o, A, Barbon Jr, S, Barbon, A, Pollonio, M, Barbin, D. “Classification of Chicken Parts Using a Portable Near-Infrared (NIR) Spectrophotometer and Machine Learning”. Applied spectroscopy 2018; 72(12):1774–1780. |
| Peres, L, Barbon Jr, S, Fuzyi, E, Barbon, A, Barbin, D, Saito, P, Andreo, N, Bridi, A. “Fuzzy approach for classification of pork into quality grades: coping with unclassifiable samples”. Computers and Electronics in Agriculture 2018; 150:455–464. |
| Santana, E, Geronimo, B, Mastelini, S, Carvalho, R, Barbin, D, Ida, E, Barbon, S. “Predicting poultry meat characteristics using an enhanced multi-target regression method”. Biosystems Engineering 2018; 171:193–204. |
| Costa, V, Leon Ferreira, A, Junior, S, others. “Strict Very Fast Decision Tree: a memory conservative algorithm for data stream mining”. Pattern Recognition Letters 2018; 116:22–28. |
| Mastelini, S, Costa, V, Santana, E, Nakano, F, Guido, R, Cerri, R, Barbon, S. “Multi-Output Tree Chaining: An Interpretative Modelling and Lightweight Multi-Target Approach”. Journal of Signal Processing Systems 2019; 91(2):191–215. |
| Costa, V, Zarpelao, B, Miani, R, Junior, SOnline detection of Botnets on Network Flows using Stream Mining. In Anais do XXXVI Simp\’osio Brasileiro de Redes de Computadores e Sistemas Distribu\’\idos 2018 . |
| Barbon J\’unior, S. Dynamic Time Warping baseado na transformada wavelet (Doctoral dissertation, Universidade de S\~ao Paulo). |
| Shirado, W, Moreira, M, Palma, J, Barbon Junior, S. “Comparative study between algorithms of discrete Fourier and Wavelet Transforms”. REVISTA BRASILEIRA DE COMPUTACAO APLICADA 2015; 7(3):97–107. |
| Barbon Jr, SToward a New Generation of Log Pre-processing Methods for Process Mining. In Business Process Management Forum undefined (pp. 55). |
| Barbon Junior, S, Tavares, G, Costa, V, Ceravolo, P, Damiani, EA Framework for Human-in-the-loop Monitoring of Concept-drift Detection in Event Log Stream. In Companion Proceedings of The Web Conference 2018 2018 (pp. 319–326). |
| Barbon, S, Campos, G, Tavares, G, Igawa, R, Guido, R. “Detection of Human, Legitimate Bot, and Malicious Bot in Online Social Networks Based on Wavelets”. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 2018; 14(1s):26. |
| Nakano, F, Mastelini, S, Barbon, S, Cerri, RStacking methods for hierarchical classification. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) 2017 (pp. 289–296). |
| Mastelini, S, Santana, E, Cerri, R, Barbon, SDSTARS: a multi-target deep structure for tracking asynchronous regressor stack. In 2017 Brazilian Conference on Intelligent Systems (BRACIS) 2017 (pp. 19–24). |
| Junior, S, Tavares, G, Ceravolo, P, Damiani, EA Framework for Trace Clustering and Concept-drift Detection in Event Streams.. In SIMPDA 2017 (pp. 153–154). |
| Pereira, L, Barbon Jr, S, Valous, N, Barbin, D. “Predicting the ripening of papaya fruit with digital imaging and random forests”. Computers and Electronics in Agriculture 2018; 145:76–82. |
| Alvarenga, S, Barbon Jr, S, Miani, R, Cukier, M, Zarpel\~ao, B. “Process mining and hierarchical clustering to help intrusion alert visualization”. Computers & Security 2018; 73:474–491. |
| Costa Barbon, A, Barbon Jr, S, Campos, G, Seixas Jr, J, Peres, L, Mastelini, S, Andreo, N, Ulrici, A, Bridi, A. “Development of a flexible Computer Vision System for marbling classification”. Computers and Electronics in Agriculture 2017; 142:536–544. |
| Tavares, G, Mastelini, S, Barbon Jr, S. “User classification on online social networks by post frequency”. CEP 2017; 86057:970–977. |
| Pena, E, Carvalho, L, Barbon Jr, S, Rodrigues, J, Proen\cca Jr, M. “Anomaly detection using the correlational paraconsistent machine with digital signatures of network segment”. Information Sciences 2017; 420:313–328. |
| Santana, E, Mastelini, S, othersDeep regressor stacking for air ticket prices prediction. In Anais do XIII Simp\’osio Brasileiro de Sistemas de Informa\cc\~ao 2017 (pp. 25–31). |
| Ceravolo, P, Damiani, E, Torabi, M, Barbon, SToward a New Generation of Log Pre-processing Methods for Process Mining. In International Conference on Business Process Management 2017 (pp. 55–70). |
| Costa, V, Barbon, S, Miani, R, Rodrigues, J, Zarpel\~ao, BDetecting mobile botnets through machine learning and system calls analysis. In 2017 IEEE International Conference on Communications (ICC) 2017 (pp. 1–6). |
| Mastelini, S, Silva, M, Costa Barbon, A, Barbon Jr, SMarbling Grading Framework Applied on Meat Boutique Environment. In Anais do XII Simp\’osio Brasileiro de Sistemas de Informa\cc\~ao 2016 (pp. 542–549). |
| Almeida, A, Barbon Jr, S, Igawa, R, Paraiso, E, Moriguchi, S. “Opinion Mining: A Comparison of Hybrid Approaches”. no. c 2016:1–7. |
| Kummer, L, Binder, F, Barbon Jr, S, Nievola, J, Paraiso, E. “Predi\cc\~ao do Estágio de Nicho em Jogos RPG Massivos de Multijogadores utilizando o Comprometimento”. Encontro Nacional de Intelig\^encia Artificial e Computacional (ENIAC) 2016:289–300. |
| Barbon Jr, S, Barbon, A, Mantovani, R, Barbin, D. “Comparison of SVM and REPTree for classification of poultry quality”. Proceedings of the Modelling, Simulation and Identification/Intelligent Systems and Control–2016 (MSI 2016):840–039. |
| Fuzyi, E, Silva, M, Barbon Jr, SM-Health Solution on pre-diagnosis of larynx. In Proceedings of the annual conference on Brazilian Symposium on Information Systems: Information Systems: A Computer Socio-Technical Perspective-Volume 1 2015 (pp. 501–508). |
| Barbon Jr, S, Tavares, G, Kido, G. “Artificial and natural topic detection in online social networks”. iSys-Revista Brasileira de Sistemas de Informa\cc\~ao 2017; 10(1):80–98. |
| Campos, G, Barbon, S, Mantovani, RA meta-learning approach for recommendation of image segmentation algorithms. In 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) 2016 (pp. 370–377). |
| Kido, G, Igawa, R, Barbon Jr, STopic modeling based on louvain method in online social networks. In Anais do XII Simp\’osio Brasileiro de Sistemas de Informa\cc\~ao 2016 (pp. 353–360). |
| Barbon Jr, S. Dynamic time warping baseado na transformada wavelet. 2007. 113p (Doctoral dissertation, Disserta\ccao (Mestrado). Instituto de F\isica de Sao Carlos da Universidade de~…). |
| Kawakani, C, Barbon, S, Miani, R, Cukier, M, Zarpel\~ao, B. “Discovering attackers past behavior to generate online hyper-alerts”. iSys-Brazilian Journal of Information Systems 2017; 10(1):122–147. |
| Almeida, A, Barbon, S, Paraiso, EMulti-class Emotions classification by Sentic Levels as features in Sentiment Analysis. In 2016 5th Brazilian Conference on Intelligent Systems (BRACIS) 2016 (pp. 486–491). |
| Shirado, W, Abreu Moreira, M, Palma, J, J\’unior, S. “Estudo comparativo entre algoritmos das transformadas discretas de Fourier e Wavelet”. Revista Brasileira de Computa\cc\~ao Aplicada 2015; 7(3):97–107. |
| Barbon, S, Igawa, R, Zarpel\~ao, B. “Authorship verification applied to detection of compromised accounts on online social networks”. Multimedia Tools and Applications 2017; 76(3):3213–3233. |
| Barbon, A, Barbon Jr, S, Mantovani, R, Fuzyi, E, Peres, L, Bridi, A. “Storage time prediction of pork by Computational Intelligence”. Computers and Electronics in Agriculture 2016; 127:368–375. |
| Kawakani, C, Junior, S, Miani, R, Cukier, M, Zarpel\~ao, BIntrusion alert correlation to support security management. In Anais do XII Simp\’osio Brasileiro de Sistemas de Informa\cc\~ao 2016 (pp. 313–320). |
| Barbin, D, Mastelini, S, Barbon Jr, S, Campos, G, Barbon, A, Shimokomaki, M. “Digital image analyses as an alternative tool for chicken quality assessment”. Biosystems Engineering 2016; 144:85–93. |
| Igawa, R, Almeida, A, Zarpel\~ao, B, Barbon Jr, S. “Recognition on Online Social Network by user’s writing style”. iSys-Revista Brasileira de Sistemas de Informa\cc\~ao 2016; 8(3):64–85. |
| Campos, G, Igawa, R, Seixas, J, Almeida, A, Guido, R, Barbon, SSupervised approach for indication of contrast enhancement in application of image segmentation. In Eighth Int. Conf. on Advances in Multimedia 2016 (pp. 12–18). |
| Carvalho, L, Barbon Jr, S, Souza Mendes, L, Proenca Jr, M. “Unsupervised learning clustering and self-organized agents applied to help network management”. Expert Systems with Applications 2016; 54:29–47. |
| Guido, R, Barbon, S, Vieira, L, Sanchez, F, Maciel, C, Pereira, J, Scalassara, P, Fonseca, EIntroduction to the discrete shapelet transform and a new paradigm: Joint time-frequency-shape analysis. In 2008 IEEE International Symposium on Circuits and Systems 2008 (pp. 2893–2896). |
| Igawa, R, Almeida, A, Zarpelao, B, Barbon Jr, SRecognition of compromised accounts on twitter. In Proceedings of the annual conference on Brazilian symposium on information systems: information systems: a computer socio-technical perspective 2015 (pp. 9–14). |
| MORIGUCHI, S, BARBON JR, S, MURAKAMI, LBuilding relationship quality in electronic commerce. In Proceedings of the IAMOT 2015-24th International Association for Management of Technology Conference: Technology, Innovation and Management for Sustainable Growth 2015 (pp. 2131–2150). |
| Igawa, R, Barbon Jr, S, Paulo, K, Kido, G, Guido, R, J\’unior, M, Silva, I. “Account classification in online social networks with LBCA and wavelets”. Information Sciences 2016; 332:72–83. |
| Alvarenga, S, Zarpel\~ao, B, Junior, S, Miani, R, Cukier, MDiscovering attack strategies using process mining. In The Eleventh Advanced International Conference on Telecommunications 2015 (pp. 119–125). |
| Seixas, J, Barbon, S, Mantovani, RPattern recognition of lower member skin ulcers in medical images with machine learning algorithms. In 2015 IEEE 28th International Symposium on Computer-Based Medical Systems 2015 (pp. 50–53). |
| Seixas, J, Barbon, S, Siqueira, C, Dias, I, Castaldin, A, Felinto, AColor energy as a seed descriptor for image segmentation with region growing algorithms on skin wound images. In 2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom) 2014 (pp. 387–392). |
| Fuzyi, E, Armani, A, Junior, S. “Modelagem de banco de dados para diagn\’ostico de transtornos mentais pela voz”. Journal of Health Informatics 2015; 7(1). |
| Pena, E, Carvalho, L, Barbon, S, Rodrigues, J, Proen\cca, MCorrelational paraconsistent machine for anomaly detection. In 2014 IEEE global communications conference 2014 (pp. 551–556). |
| Guido, R, Pereira, J, Fonseca, E, Maciel, C, Vieira, L, Guilerme, F, Barbon, SSupport vector machines and wavelets for voice disorder sorting. In 2006 Proceeding of the Thirty-Eighth Southeastern Symposium on System Theory 2006 (pp. 434–438). |
| Junior, S, Moriguchi, S, Souza, A. “Contribui\cc\~oes da Interface Usuário-Computador nos servi\ccos hospitalares”. Journal of Health Informatics 2013; 5(4). |
| Fantinato, P, Guido, R, Chen, SH, Santos, B, Vieira, L, J\’unior, S, Rodrigues, L, Sanchez, F, Souza, L, Maciel, C, othersA fractal-based approach for speech segmentation. In 2008 Tenth IEEE International Symposium on Multimedia 2008 (pp. 551–555). |
| Igawa, R, Kido, G, Seixas, J, Barbon, SAdaptive distribution of vocabulary frequencies: A novel estimation suitable for social media corpus. In 2014 Brazilian Conference on Intelligent Systems 2014 (pp. 282–287). |
| Pena, E, Barbon, S, Rodrigues, J, Lemes Proenca Junior, MAnomaly detection using digital signature of network segment with adaptive ARIMA model and Paraconsistent Logic. In Computers and Communication (ISCC), 2014 IEEE Symposium on 2014 (pp. 1–6). |
| Carvalho, L, Rodrigues, J, Barbon, S, Proen\cca, MUsing ant colony optimization metaheuristic and dynamic time warping for anomaly detection. In 2013 21st International Conference on Software, Telecommunications and Computer Networks-(SoftCOM 2013) 2013 (pp. 1–5). |
| Guido, R, Maciel, C, Monteiro, M, Fonseca, E, Panchapagesan, S, Pereira, J, Vieira, L, Junior, S, Guilherme, M, Sergio, K, othersA study on the best wavelet for audio compression. In 2006 Fortieth Asilomar Conference on Signals, Systems and Computers 2006 (pp. 2115–2118). |
| Rodrigues, L, Souza, T, Barbon Jr, S, Lucatto Jr, RGerenciamento de Prontuário Eletr\^onico de Pacientes em Cl\’\inica de Atendimento Fisioterápico do Centro Universitário do Norte Paulista. In XVIII Congresso Brasileiro de Informática em Sa\’ude 2006 . |
| Guido, R, Barbon Jr, S, Solgon, R, Paulo, K, Rodrigues, L, Da Silva, I, Escola, J. “Introducing the discriminative paraconsistent machine (dpm)”. Information Sciences 2013; 221:389–402. |
| Silva, R, Mazucato, S, Barbon Jr, SEstudo sobre os par\^ametros ac\’usticos relevantes para a verifica\ccao de patologias associadasa voz. In Anais do Congresso de Matemática Aplicada e Computacional-CMAC Sudeste undefined (pp. 282–285). |
| Barbon Jr, S, Guido, R, Vieira, L, Fonseca, E, Sanchez, F, Scalassara, P, Maciel, C, Pereira, J, Chen, SH. “Wavelet-based dynamic time warping”. Journal of Computational and Applied Mathematics 2009; 227(2):271–287 |
| Saito, E, Rojas, G, Ricz, L, Junior, S. “Reconhecimento automático de Distonia Lar\’\ingea com base na sustenta\cc\~ao do Pitch”. Journal of Health Informatics 2014; 6(2). |
| Barbon J\’unior, S. Identifica\cc\~ao de patologias na laringe com base na Discriminative Paraconsistent Machine (DPM) (Doctoral dissertation, Universidade de S\~ao Paulo). |
| J\’unior, S, Carlos-SP-Brasil, S. Dynamic time warping baseado na transformada wavelet (Doctoral dissertation, Disserta\cc\~ao (Mestrado)—Instituto de F\’\isica de S\~ao Carlos-Universidade de~…). |
| Sanchez, F, J\’unior, S, Vieira, L, Guido, R, Fonseca, E, Scalassara, P, Maciel, C, Pereira, J, Chen, SH. “Wavelet-based cepstrum calculation”. Journal of Computational and Applied Mathematics 2009; 227(2):288–293. |
| J\’unior, S, Guido, R, Vieira, LA Neural-Network Approach for Speech Features Classification Based on Paraconsistent Logic. In 2009 11th IEEE International Symposium on Multimedia 2009 (pp. 567–570). |
| Guido, R, Chen, SH, Junior, S, Souza, L, Vieira, L, Rodrigues, L, Escola, J, Zulato, P, Lacerda, M, Ribeiro, JOn the Determination of Epsilon during Discriminative GMM Training. In 2010 IEEE International Symposium on Multimedia 2010 (pp. 362–364). |
| Guido, R, Vieira, L, J\’unior, S, Sanchez, F, Maciel, C, Fonseca, E, Pereira, J. “A neural-wavelet architecture for voice conversion”. Neurocomputing 2007; 71(1-3):174–180. |
| Guido, R, Vieira, L, Junior, S, Sanchez, F, Guilherme, M, Sergio, K, Scarpa, T, Fonseca, E, Pereira, J, Monteiro, MA Fractal and Wavelet-Based Approach for Audio Coding.. In Eighth IEEE International Symposium on Multimedia (ISM’06) 2006 (pp. 253–256). |
| Junior, S, Guido, R, Chen, SH, Vieira, L, Sanchez, F, Improved dynamic time warping based on the discrete wavelet transform. In Ninth IEEE International Symposium on Multimedia Workshops (ISMW 2007) 2007 (pp. 256–263). |
| Guido, R, Junior, S, Vieira, L, Sanchez, F, Maciel, C, Scalassara, P, Pereira, J, Puia, V. “Spoken document summarization based on dynamic time warping and wavelets”. International Journal of Semantic Computing 2007; 1(03):347–357. |
| Scalassara, P, Maciel, C, Guido, R, Pereira, J, Fonseca, E, Montagnoli, A, J\’unior, S, Vieira, L, Sanchez, F. “Autoregressive decomposition and pole tracking applied to vocal fold nodule signals”. Pattern recognition letters 2007; 28(11):1360–1367. |
Home » Publications