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.

Google Scholar | Scopus

Current Projects

Contact

Email: sylvio.barbonjunior@units.it

GitHub: github.com/sbarbonjr

LinkedIn: linkedin.com/in/barbon

Publications

Show/Hide Publications (2018–2026)

2026

  • Grigore, I.M. et al. ProVEx: A Contrastive Explanation Framework for Process Variant Analysis. SSRN 6963087, 2026.
  • Pereira, E.P. et al. Auditable Flood Attack Detection using Isolation Forest with Decision Predicate Graphs. SBRC, 2026.
  • Ribeiro, J.V. et al. Spectral Model eXplainer: a chemically-grounded explainability framework for spectral-based machine learning models. arXiv:2605.02684, 2026.
  • Malina, G.I. et al. A Design-Oriented Process Mining Framework for Railway Operations. Information, 17(5), 483, 2026.
  • di Lauro, L. et al. A Comparative Study of Federated Learning Frameworks for IoT-Driven Smart Cities. Semina: Ciências Exatas e Tecnológicas, 47, 2026.
  • Moradbeikie, A. et al. Sensor2EventLog: Bridging Continuous IoT Data and Process Mining through Eventization. CAiSE, 177-194, 2026.
  • Valentino, M. et al. Image-Based Machine Learning for Predicting Acceptability Limits in Frozen Pizza Shelf Life. Foods, 15(8), 1348, 2026.
  • Arrighi, L. et al. Explainable artificial intelligence techniques for interpretation of food models: a review. Artificial Intelligence Review, 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.
  • Grigore, I.M. et al. Revealing Trace Variant Shift via Multi-dimensional Profiling and Community-Aware Graph Modeling. International Conference on Business Process Management (BPM), 15–27, 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.
  • 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

  • Gomes Mantovani, R. et al. Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms. Data Mining and Knowledge Discovery, 38(3), 1364–1416, 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.
  • Grigore, I.M. et al. Beyond Flattening: Detecting Concurrency Anomalies Using K-NN Graph-Based Modeling in Object-Centric Event Logs. From Data to Models and Back, 116–136, 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.
  • Oyamada, R.S. et al. A scikit-learn extension dedicated to process mining purposes. CEUR Workshop Proceedings, 3552, 11–15, 2023.
  • de Souza Schiaber, P. et al. Spectral Characteristics Analysis of Electromyography Signals Recorded During Dynamic Contractions. International Conference on Control, Decision and Information Technologies (CoDIT), 2023.
  • Raj, D.R.K. et al. Classification of carambola (Averrhoa carambola L.) according to the maturation stage using computer vision. Galoá, 2023.
  • Barbon Junior, S., Peres, S.M. Mineração de Processos e Aprendizado de Máquina: Conquistas distribuídas, mas Desafios Compartilhados. Computação Brasil, 20–24, 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.
  • 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.
  • 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.
  • Nakagawa, F.H.Y. et al. Attack Detection in Smart Home IoT Networks using CluStream and Page-Hinkley Test. IEEE LATINCOM, 1–6, 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.
  • Fonseca, E.S. et al. Acoustic investigation of speech pathologies based on the discriminative paraconsistent machine (DPM). Biomedical Signal Processing and Control, 55, 101615, 2020.
  • Tavares, G.M., Barbon Jr, S. Analysis of language inspired trace representation for anomaly detection. TPDL, 296–308, 2020.
  • Gomes Mantovani, R. et al. Rethinking Default Values: a Low Cost and Efficient Strategy to Define Hyperparameters. arXiv:2008.00025, 2020.
  • de Morais, J.I. et al. A Multi-label Classification System to Distinguish among Fake, Satirical, Objective and Legitimate News in Brazilian Portuguese. iSys, 13(4), 126–149, 2020.
  • Omori, N.J. et al. Comparing concept drift detection with process mining software. iSys – Brazilian Journal of Information Systems, 13(4), 101–125, 2020.
  • Zarpelão, B.B. et al. How machine learning can support cyberattack detection in smart grids. Artificial Intelligence Techniques for a Scalable Energy Transition, 2020.
  • Junior, S.B. et al. Advantages of Multi-Target Modelling for Spectral Regression. Spectroscopic Techniques & Artificial Intelligence for Food and Beverage, 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.
  • de Morais, J.I. et al. Deciding among Fake, Satirical, Objective and Legitimate news: A multi-label classification system. SBSI, 1–8, 2019.
  • Tavares, G.M. et al. Leveraging Anomaly Detection in Business Process with Data Stream Mining. iSys – Brazilian Journal of Information Systems, 12(1), 54–75, 2019.
  • Mastelini, S.M. et al. Online Multi-target regression trees with stacked leaf models. arXiv:1903.12483, 2019.
  • da Costa, V.G.T. et al. Online Local Boosting: improving performance in online decision trees. BRACIS, 132–137, 2019.
  • Prece, B. et al. Improvements on diagnostic assessment questionnaires of Maturity Level Management with feature selection. SBSI, 1–8, 2019.
  • Santana, E.J. et al. Stock Portfolio Prediction by Multi-Target Decision Support. iSys – Brazilian Journal of Information Systems, 12(1), 5–27, 2019.

2018

  • Barbon Jr, S. et al. Machine learning applied to near-infrared spectra for chicken meat classification. Journal of Spectroscopy, 2018(1), 8949741, 2018.
  • da Costa, V.G.T. et al. Strict Very Fast Decision Tree: a memory conservative algorithm for data stream mining. Pattern Recognition Letters, 116, 22–28, 2018.
  • Barbon Junior, S. et al. A Framework for Human-in-the-loop Monitoring of Concept-drift Detection in Event Log Stream. Companion Proceedings of the Web Conference 2018, 319–326, 2018.
  • Barbon, S.J. et al. Detection of Human, Legitimate Bot, and Malicious Bot in Online Social Networks Based on Wavelets. ACM Transactions on Multimedia Computing, Communications, and Applications, 2018.
  • Almeida, A.M.G. et al. Applying multi-label techniques in emotion identification of short texts. Neurocomputing, 320, 35–46, 2018.
  • Bezerra, V.H. et al. Providing IoT host-based datasets for intrusion detection research. Anais do XVIII Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais, 2018.
  • Nolasco Perez, I.M. et al. Classification of chicken parts using a portable near-infrared (NIR) spectrophotometer and machine learning. Applied Spectroscopy, 72(12), 1774–1780, 2018.
  • de Alvarenga, S.C. et al. Process mining and hierarchical clustering to help intrusion alert visualization. Computers & Security, 73, 474–491, 2018.
  • Santana, E.J. et al. Predicting poultry meat characteristics using an enhanced multi-target regression method. Biosystems Engineering, 171, 193–204, 2018.
  • Basantia, N.C. et al. Hyperspectral Imaging Analysis and Applications for Food Quality. CRC Press, 2018.
  • Bezerra, V.H. et al. One-class classification to detect botnets in IoT devices. Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais, 2018.
  • Nakano, F.K. et al. Improving Hierarchical Classification of Transposable Elements using Deep Neural Networks. IJCNN, 1–8, 2018.
  • Tavares, G.M. et al. Anomaly detection in business process based on data stream mining. SBSI, 1–8, 2018.
  • Mastelini, S.M. et al. Benchmarking Multi-target Regression Methods. BRACIS, 396–401, 2018.
  • Junior, S.B. et al. U-Healthcare System for Pre-Diagnosis of Parkinson's Disease from Voice Signal. IEEE International Symposium on Multimedia (ISM), 271–274, 2018.
  • Mastelini, S.M. et al. Computer vision system for characterization of pasta (noodle) composition. Journal of Electronic Imaging, 27(5), 053021, 2018.
  • Peres, L.M. et al. Fuzzy approach for classification of pork into quality grades: coping with unclassifiable samples. Computers and Electronics in Agriculture, 150, 455–464, 2018.
  • da Silva, J.A.P.R. et al. Stock Portfolio Prediction by Multi-Target Decision Support. SBSI, 1–8, 2018.
  • da Costa, V.G.T. et al. Online detection of botnets on network flows using stream mining. SBRC, 2018.
  • da Costa, V.G.T. et al. Making Data Stream Classification Tree-Based Ensembles Lighter. BRACIS, 480–485, 2018.
  • Artoni, A.A. et al. Aplicação de aprendizado de máquina para auxílio no diagnóstico do Transtorno do Espectro do Autismo em adultos. Nuevas Ideas en Informática Educativa, 14, 167–73, 2018.
Livro: Inteligência Artificial em Ciência Animal