Sylvio Barbon Junior

About Me

Hi, I'm Sylvio Barbon Junior, a researcher and professor working at the intersection of Artificial Intelligence ( University of Trieste, Italy), Process Mining, and Data Science. My academic and professional journey has been shaped by curiosity about how data can reveal insights into complex processes and support better decision-making. Over the years, I have been involved in projects that span both academia and industry, ranging from methodological research to applied solutions that address practical challenges. I particularly enjoy collaborating in multidisciplinary environments, where diverse perspectives often lead to innovative outcomes. Teaching and mentoring are also central to my work, as I value the opportunity to support students and colleagues in developing their skills and pursuing their own research paths. What drives me most is the chance to connect theory with practice—whether by developing models, analyzing data, or building tools that have tangible impact. Through this balance of research, teaching, and applied projects, I aim to contribute to advancing knowledge while also creating value beyond the academic setting.

Current Projects

Contact

Email: sylvio.barbonjunior@units.it

GitHub: github.com/sbarbonjr

LinkedIn: linkedin.com/in/barbon

Publications

Show/Hide Publications (2018–2026)

2026

  • Soares, J.M.L. et al. Conjunto de dados associado ao desenvolvimento de modelos de aprendizado de máquina para predição dos potenciais de eletro-oxidação de álcoois. 2026.
  • Moradbeikie, A. et al. Real-time and explainable non-destructive nut classification using spike-triggered acoustic sensing. Computers and Electronics in Agriculture, 244, 111502, 2026.
  • da Silva, M.C. et al. Close to Reality: Interpretable and Feasible Data Augmentation for Imbalanced Learning. arXiv:2603.13927, 2026.
  • Ribeiro, J.V. et al. Explainability in vis-NIR and XRF-based modeling for soil fertility analysis: A comprehensive review. TrAC Trends in Analytical Chemistry, 118776, 2026.
  • da Silva, M.C. et al. Explaining AutoClustering: Uncovering Meta-Feature Contribution in AutoML for Clustering. arXiv:2602.18348, 2026.
  • da Costa Barbon, A.P.A. et al. Process Analytical Technologies applied to Quality Control of Emerging Alternative Protein Food Products: Challenges and Future Trends. Journal of Pharmaceutical and Biomedical Analysis Open, 100105, 2026.
  • Barbon Junior, S. et al. Encoding Techniques for Digital Trace Data. Digital Trace Data Research in Information Systems: Foundations, Methods, 2026.
  • Moraes, I.A. et al. Explainable artificial intelligence (XAI) applied to deep computer vision for the assessment and classification of oleogels with varying oleogelator types and concentrations. Microchemical Journal, 116821, 2026.
  • da Costa Barbon, A.P.A., Barbon Junior, S. Inteligência Artificial em Ciência Animal: Aplicações em Pesquisa e na Indústria. Santa Cruz do Sul: Essere nel Mondo, 2026. ISBN 978-65-5790-123-6.

2025

  • Raj, D.R.K. et al. Impedance based electronic tongue applied for sensory profiling of black tea with sweeteners. Journal of Food Composition and Analysis, 108812, 2025.
  • da Silva, M.C. et al. TPOT-Clustering. 2025 International Joint Conference on Neural Networks (IJCNN), 1–8, 2025.
  • Zanin, G. et al. Direction-Aware Room Impulse Response Estimation for Immersive Audio Rendering in Real Environments. Proceedings of the 33rd ACM International Conference on Multimedia, 8116–8124, 2025.
  • Moradbeikie, A. et al. Process Mining of Sensor Data for Predictive Process Monitoring: A HACCP-Guided Pasteurization Study Case. Systems, 13(11), 935, 2025.
  • Grigore, I.M. et al. Towards Trace Variant Explainability. Advances in Databases and Information Systems: 29th European Conference (ADBIS), 2025.
  • Arrighi, L. et al. Discriminating Short-Term Moisture Changes in Stuffed Pasta Using Deep Computer Vision. International Conference on Image Analysis and Processing (ICIAP), 489–496, 2025.
  • Arrighi, L. et al. End-to-End Explainability of Machine Learning Pipelines with Decision Predicate Graphs: A Financial Scenario Case Study. CEUR Workshop Proceedings, 2025.
  • Grigore, I.M. et al. Detecting Anomalies in Healthcare Processes: A K-NN Graph-Based approach. CEUR Workshop Proceedings, 2025.
  • Pereira, E.P. et al. Learning to Explain Cyberattacks: Insights from Random Forest and Decision Predicate Graphs. CEUR Workshop Proceedings, 2025.
  • de Souza Schiaber, P. et al. Analyzing fatigue in dynamic exercise through electromyography signals and similarity metrics. Biomedical Signal Processing and Control, 99, 106864, Elsevier, 2025.
  • von Zuben, T.W. et al. Machine learning predictions of onset and oxidation potentials for methanol and ethanol electrooxidation: Comprehensive analysis and experimental validation. Electrochimica Acta, 509, 145285, 2025.
  • Moraes, I.A. de et al. Assessment of oleogel stability over storage. 2025.
  • Moraes, I.A. de et al. Explainable artificial intelligence (XAI) applied to deep computer vision of microscopy imaging and spectroscopy for assessment of oleogel stability over storage. Journal of Food Engineering, 394, 112515, Elsevier, 2025.
  • Moraes, I.A. et al. Predicting oleogels properties using non-invasive spectroscopic techniques and machine learning. Food Research International, 207, 116044, Elsevier, 2025.
  • Moraes, I.A. de et al. Physicochemical and structural analysis of oleogels using non-invasive techniques. 2025.
  • Moraes, I.A. et al. Authentication of coriander oil and adulterant identification using electronic nose and spectroscopic techniques. Food Chemistry, 483, 144196, Elsevier, 2025.
  • Arrighi, L. et al. Explainable Artificial Intelligence techniques for interpretation of food datasets: a review. arXiv:2504.10527, 2025.
  • Lopes, J.F. et al. Online Meta-Recommendation of CUSUM Hyperparameters for Enhanced Drift Detection. Sensors, 25(9):2787, MDPI, 2025.
  • Ceschin, M. et al. Extending Decision Predicate Graphs for Comprehensive Explanation of Isolation Forest. World Conference on Explainable Artificial Intelligence, 271–293, 2025.
  • Guido, R.C., Barbon Junior, S. Beyond accuracy: The need for explainable AI in biomedical voice technology. Computers in Biology and Medicine, 192, 110240, Elsevier, 2025.
  • Soares, J.M.L. et al. Predicting Glycerol Electrochemical Oxidation Potentials Using Machine Learning. J. Braz. Chem. Soc., 36(8), e–20250090, 2025.
  • Berti, M. et al. Meta-learning approach for variational autoencoder hyperparameter tuning. Journal of Universal Computer Science, 31(7):668–682, 2025.

2024

  • Ceravolo, P. et al. Tuning ML to Address Process Mining Requirements. IEEE Access, 12, 24583–24595, 2024.
  • Silva, R.P. et al. Unsupervised tuning for drift detectors. IEEE Access, 12, 54256–54271, 2024.
  • Grigore, I.M. et al. Automated Trace Clustering Pipeline Synthesis. Information, 15(4):241, 2024.
  • Oyamada, R.S. et al. Enhancing Predictive Process Monitoring with Time Features. CAiSE, 71–86, Springer, 2024.
  • Junior, S.B. et al. Are LLMs the New Interface for Data Pipelines?. BiDEDE@SIGMOD, ACM, 2024.
  • Oyamada, R.S. et al. CoSMo: Conditioned Process Simulation Models. BPM, 328–344, 2024.
  • da Silva, M.C. et al. Benchmarking AutoML Clustering Frameworks. AutoML Conf., 2024.
  • de Moraes, I.A. et al. Interpreting carambola maturity with computer vision. Food Research Int., 192, 114836, 2024.
  • Junior, S.B. et al. Data-Driven Methods for Soccer Analysis. Springer AI in Sports, 233, 2024.
  • da Silva Ferreira, M.V. et al. XAI for dragon fruit classification. Scientia Horticulturae, 338, 113605, 2024.
  • Vecchi, L.P. et al. Tuning Hypothesis Creation for Hate Speech Detection. BRACIS, 253–268, 2024.
  • Arrighi, L. et al. Decision Predicate Graphs in Tree Ensembles. World Conf. on XAI, 311–332, 2024.
  • de Souza, R.A. et al. Forecasting Malware Incident Rates. AINA, 226–237, 2024.
  • da Silva Pereira, E. et al. Portable NIR spectrometer for mastitis detection. Food Control, 163, 110527, 2024.
  • da Silva, M.C. et al. Problem-oriented AutoML in Clustering. arXiv:2409.16218, 2024.
  • Sakurai, G.Y. et al. A Self-Tuning Ensemble for Drift Detection. ENIAC, 811–822, 2024.

2023

  • Oyamada, R.S. et al. Meta-learning framework for graph similarity search. Information Systems, 112, 102123, 2023.
  • Martins, V.E. et al. Meta-learning for tuning active learning in streams. Pattern Recognition, 138, 109359, 2023.
  • Silva, M.C. et al. Using Process Mining to Reduce Fraud. FinTech, 2(1):120–137, 2023.
  • Vitor, A.L.O. et al. Induction motor fault diagnosis with ML. Expert Systems with Applications, 224, 119998, 2023.
  • Sakurai, G.Y. et al. Benchmarking Drift Detector Algorithms. Future Internet, 15(5):169, 2023.
  • Junior, S.B. et al. Multiple voice disorders: multi-label approaches. Speech Communication, 152, 102952, 2023.
  • Abonizio, H.Q. et al. CoronaAI: chatbot against disinformation. Int. J. Med. Informatics, 177, 105134, 2023.
  • Tavares, G.M. et al. Anomaly detection with encoding techniques. CSIS, 20(3):1207–1233, 2023.
  • Tavares, G.M. et al. Trace encoding in process mining: survey. Eng. Apps. of AI, 126, 107028, 2023.
  • Arrighi, L. et al. Explainable Automated Anomaly Recognition. World Conf. on XAI, 420–432, 2023.
  • de Castro Silva, V. et al. Explainable Time Series Tree. IEEE Access, 11, 120845–120856, 2023.

2022

  • Aguiar, G.J. et al. Using meta-learning for multi-target regression. Information Sciences, 584, 665–684, 2022.
  • Scaranti, G.F. et al. Unsupervised anomaly detection in SDN. Expert Systems with Applications, 191, 116225, 2022.
  • Barbon Junior, S. et al. Sport action mining: dribbling recognition in soccer. Multimedia Tools & Applications, 81(3):4341–4364, 2022.
  • Tavares, G.M. et al. Automating process discovery with meta-learning. CoopIS, 205–222, Springer, 2022.
  • Tavares, G.M. et al. Selecting optimal trace clustering pipelines with meta-learning. BRACIS, 150–164, 2022.
  • Alberghini, G. et al. Adaptive ensembles for drifting data streams. Neurocomputing, 481, 228–248, 2022.
  • Lopes, J.F. et al. Deep computer vision system for cocoa classification. Multimedia Tools & Applications, 81(28):41059–41077, 2022.

2021

  • Santana, E.J. et al. Improved soil prediction with multi-target stacked models. Chemometrics & Intelligent Lab. Systems, 104231, 2021.
  • Oliveira, M.M. et al. Classification of fermented cocoa beans. J. Food Composition & Analysis, 97, 103771, 2021.
  • Zarpelão, B. et al. Blockchain for agrifood traceability. Food Authentication & Traceability, 279–302, 2021.
  • Barbon Junior, S. et al. Using meta-learning to recommend process discovery methods. arXiv:2103.12874, 2021.
  • Vertuam Neto, R. et al. Online clustering for anomaly detection. SBIS, 2021.
  • Azzini, A. et al. Advances in Data Management in the Big Data Era. Springer, 99–126, 2021.
  • Peres, L.M. et al. Meta-recommendation of pork quality standards. Biosystems Eng., 210, 13–19, 2021.
  • Tavares, G.M., Barbon, S.B. Encoding via meta-learning for anomaly detection. ADBIS, 157–168, 2021.
  • Barbon, S.B. et al. Selecting optimal trace clustering pipelines with AutoML. arXiv:2109.00635, 2021.
  • Abonizio, H.Q. et al. Text data augmentation for sentiment analysis. IEEE Trans. AI, 3(5):657–668, 2021.
  • Santana, E.J. et al. Adversarial examples in regression forecasting. Information, 12(10):394, 2021.
  • Silva, R.P. et al. Time series segmentation via stationarity analysis. Sensors, 21(21):7333, 2021.
  • Caetano, F.G. et al. Football player dominant regions model. Scientific Reports, 11:18209, 2021.

2020

  • Junior, S.B. et al. Multi-target prediction of wheat flour quality. Information Processing in Agriculture, 7(2):342–354, 2020.
  • Lopes, J.F. et al. Dual Stage Image Analysis for ham defects. Biosystems Eng., 191:129–144, 2020.
  • Campos, G.F.C. et al. Robust CV system for meat segmentation. ELCVIA, 19(1):15–27, 2020.
  • Lopes, J.F. et al. Evaluating trade-offs for data stream classification. IEEE Trans. Net. & Serv. Mgmt, 17(2):1013–1025, 2020.
  • Abonizio, H.Q. et al. Language-independent fake news detection. Future Internet, 12(5):87, 2020.
  • Scaranti, G.F. et al. AI systems + fuzzy logic for SDN attacks. IEEE Access, 8:100172–100184, 2020.
  • Ceravolo, P. et al. Evaluation goals for online process mining. IEEE TSC, 15(4):2473–2489, 2020.
  • Junior, S.B. et al. Anomaly Detection on Event Logs with Few Labels. ICPM, 161–168, IEEE, 2020.
  • Mastelini, S.M. et al. DSTARS: Deep Structure for regressor stacking. Applied Soft Computing, 106215, 2020.
  • Queiroz, H. et al. Pre-trained data augmentation for text classification. BRACIS, 551–565, 2020.
  • Santana, E.J. et al. Photovoltaic generation forecast under adversarial attacks. BRACIS, 634–649, 2020.
  • Martins, V.E. et al. Active learning embedded in incremental decision trees. BRACIS, 367–381, 2020.
  • Junior, S.B. et al. Evaluating Trace Encoding Methods in Process Mining. DataMod, 174, Springer, 2020.
  • Oyamada, R.S. et al. Proximity graph auto-configuration via meta-learning. ADBIS, 93–107, 2020.

2019

  • Mastelini, S.M. et al. Multi-output tree chaining for multi-target regression. J. Signal Processing Systems, 91(2):191–215, 2019.
  • Kato, T. et al. White striping degree assessment in chicken. Asian-Australasian J. Animal Sciences, 32(7):1015, 2019.
  • Geronimo, B.C. et al. CV + NIR for wooden breast classification. Infrared Physics & Technology, 96:303–310, 2019.
  • Campos, G.F.C. et al. ML hyperparameter selection for CLAHE. EURASIP JIVP, 2019(1):59.
  • Nolasco, I.M. et al. Comparison of rapid techniques for ground meat classification. Biosystems Eng., 183:151–159, 2019.
  • Turrisi, V.G. et al. Evaluating trade-offs in stream classification. GPC, 3–17, Springer, 2019.
  • Costa, V.G. et al. Mobile botnets detection with ML. Int. J. Security & Networks, 14(2):103–118, 2019.
  • Lopes, J.F. et al. Barley flour classification with CV. Sensors, 19(13):2953, 2019.
  • Tavares, G.M. et al. Synthetic event streams. IEEE Dataport, 2019.
  • Tavares, G.M. et al. Overlapping Analytic Stages in Online Process Mining. SCC, 167–175, IEEE, 2019.
  • Aguiar, G.J. et al. Meta-learning for multi-target regression. BRACIS, 377–382, 2019.
  • Bezerra, V.H. et al. IoTDS: one-class detection of botnets. Sensors, 19(14):3188, 2019.
  • Omori, N.J. et al. Comparing drift detection with process mining tools. SBIS, 2019.

2018

  • Paula, R.S. et al. Multispectral system for meat quality evaluation. Computers and Electronics in Agriculture, 146:465–472, 2018.
  • Bezerra, V.H. et al. IoTDS: Internet of Things dataset for DDoS attacks. IEEE Dataport, 2018.
  • de Lima, F.G. et al. Sports video segmentation for highlights. Multimedia Tools & Applications, 77(10):12133–12152, 2018.
  • dos Santos, J.L. et al. Multispectral chicken meat classification. Infrared Physics & Technology, 92:341–350, 2018.
  • Oliveira, M.M. et al. Chicken meat quality classification with spectral techniques. Food Research Int., 108:245–249, 2018.
  • Simoes, J.M. et al. Quality control in poultry meat production. Food Analytical Methods, 11(6):1648–1659, 2018.
Livro: Inteligência Artificial em Ciência Animal