Books:

• Edwin Lughofer and Moamar Sayed-Mouchaweh, Predictive Maintenance in Dynamic Systems --- Advanced Methods, Decision Support Tools and Real-World Applications, opens an external URL in a new window, ca. 592 Pages, ca. 200 Illustrations, Springer New York, to appear, March 2019. (cited 52 times, Google Scholar,).

• Edwin Lughofer, Evolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications, opens an external URL in a new window, Springer Verlag, Berlin Heidelberg, 2011, ISBN: 978-3-642-18086-6, 460 pages, 148 figures, 263 images, 26 tables (cited 431 times, Google Scholar, "h").

• Moamar Sayed-Mouchaweh, Edwin Lughofer, Learning in Non-stationary Environments: Methods and Applications, opens an external URL in a new window, Springer Verlag, New York, 2012. (cited 167 times, Google Scholar, "h")

Chapters:

• E. Lughofer and M. Sayed-Mouchaweh. Prologue --- Predictive Maintenance in Dynamic Systems. Predictive Maintenance in Dynamic Systems, opens an external URL in a new window, editors: E. Lughofer and M. Sayed-Mouchaweh, Springer, pp. 1-23, 2019.
• E. Lughofer, A.C. Zavoianu, M. Pratama and T. Radauer. Automated Process Optimization in Manufacturing Systems based on Static and Dynamic Prediction Models. in: Predictive Maintenance in Dynamic Systems, opens an external URL in a new window, editors: E. Lughofer and M. Sayed-Mouchaweh, Springer, 486-531, 2019.
• M. Pratama, A. Ashfahani, E. Lughofer and S. Huang. An Evolving RFID Localization Model in The Manufacturing Shopfloor. in: Predictive Maintenance in Dynamic Systems, opens an external URL in a new window, editors: E. Lughofer and M. Sayed-Mouchaweh, Springer, pp. 287-309, 2019.
• Edwin Lughofer, Robust Data-Driven Fault Detection in Dynamic Process Environments Using Discrete Event Systems. in: Diagnosability, Security and Safety of Hybrid Dynamic and Cyber-Physical Systems, opens an external URL in a new window, editor: M. Sayed-Mouchaweh, pp. 73--116, 2018.
• Edwin Lughofer, Evolving Fuzzy Systems --- Fundamentals, Reliability, Interpretability, Useability, Applications (a comprehensive work of reference), opens an external URL in a new window, in: Handbook on Computational Intelligence, opens an external URL in a new window, editor: Plamen Parvanov Angelov, World Scientific, pp. 67-135, 2016   -  DOWNLOAD, opens an external URL in a new window
• Edwin Lughofer, Flexible Evolving Fuzzy Inference Systems from Data Streams (FLEXFIS++), in: Learning in Non-Stationary Environments: Methods and Applications, editors: Moamar Sayed-Mouchaweh and Edwin Lughofer, Springer, New York, 2012, pp. 205-246
• Edwin Lughofer, Christian Eitzinger and Carlos Guardiola, On-line Quality Control with Flexible Evolving Fuzzy Systems, in: Learning in Non-Stationary Environments: Methods and Applications, editors: Moamar Sayed-Mouchaweh and Edwin Lughofer, Springer, New York, 2012, pp. 375-406
• Davy Sannen, Jean-Michel Papy, Steve Vandenplas, Edwin Lughofer and Hendrik van Brussel, Incremental Classifier Fusion and its Application in Industrial Monitoring and Diagnostics, in: Learning in Non-Stationary Environments: Methods and Applications, editors: Moamar Sayed-Mouchaweh and Edwin Lughofer, Springer, New York, 2012, pp. 153-184
• Edwin Lughofer, Evolving Fuzzy Models - Incremental Learning, Stability and Interpretability Issues, Applications, VDM Verlag, Saarbrücken, 2008 (book issue of PhD thesis)
• Edwin Lughofer, Data-Driven Incremental Learning of Takagi-Sugeno Fuzzy Models, PhD-Thesis, Department of Knowledge-Based Mathematical Systems, University Linz, 2001-2005
• Edwin Lughofer. Towards Robust Evolving Fuzzy Systems, book chapter in Evolving Intelligent Systems - Methodologies and Applications, opens an external URL in a new window, editors: Plamen Angelov, Dimitar Filev and Nik Kasabov, John Wiley and Sons, 2010, pp. 87-126
• Erich Peter Klement*, Edwin Lughofer, Johannes Himmelbauer and Bernhard Moser, Data-Driven and Knowledge-Based Modelling, chapter in Hagenberg Research, opens an external URL in a new window, editors: Michael Affenzeller, Bruno Buchberger, Alois Ferscha, Michael Haller, Tudor Jebelean, Erich Peter Klement, Josef Kueng, Peter Paule, Birgit Proell, Wolfgang Schreiner, Gerhard Weiss, Roland Wagner, Wolfram Woess, Robert Stubenrauch and Wolfgang Windsteiger, Springer Verlag, opens an external URL in a new window, pp. 237-279, 2009
• Christian Eitzinger*, James E. Smith, Edwin Lughofer and Davy Sannen, Lernfaehige Inspektionssysteme, Automatisierungsatlas, opens an external URL in a new window, SPS Magazin, opens an external URL in a new window, 2009, pp. 370-372

• E. Lughofer and M. Pratama. Online Sequential Ensembling of Predictive Fuzzy Systems. Evolving Systems, opens an external URL in a new window, on-line and in press, 2021, https://link.springer.com/article/10.1007/s12530-021-09398-x, opens an external URL in a new window

• E. Lughofer, M. Pratama and I. Skrjanc, Online Bagging of Evolving Fuzzy Systems, Information Sciences, opens an external URL in a new window, on-line and in press, 2021, doi: https://doi.org/10.1016/j.ins.2021.04.041, opens an external URL in a new window

• E. Lughofer, Improving the Robustness of Recursive Consequent Parameters Learning in Evolving Neuro-Fuzzy Systems, Information Sciences, opens an external URL in a new window, vol. 545, pp. 555-574, 2021, https://doi.org/10.1016/j.ins.2020.09.026, opens an external URL in a new window , opens an external URL in a new window

• P.V. De Campos Souza and E. Lughofer, An Evolving Neuro-Fuzzy System based on Uni-Nullneurons with Advanced Interpretability Capabilities, Neurocomputing, opens an external URL in a new window, vol. 451, pp. 231-251, 2021, https://doi.org/10.1016/j.neucom.2021.04.065, opens an external URL in a new window
• P.V. De Campos Souza, E. Lughofer and A.J. Guimaraes, An interpretable evolving fuzzy neural network based on self-organized direction-aware data partitioning and fuzzy logic neurons, Applied Soft Computing, opens an external URL in a new window, vol. 112, ID: 107829, 2021, https://doi.org/10.1016/j.asoc.2021.107829, opens an external URL in a new window
• A. Verma and A. Singh and E. Lughofer and X. Cheng and K. Abualsaud.  Multilayered-quality education ecosystem (MQEE): an intelligent education modal for sustainable quality education, Journal of Computing in Higher Education, opens an external URL in a new window, 2021, https://doi.org/10.1007/s12528-021-09291-1, opens an external URL in a new window.
• P.V. De Campos Souza, A.J. Guimaraes, V.S. Araujo and E. Lughofer. An intelligent Bayesian hybrid approach to help Autism Diagnosis. Soft Computing, vol. 65, pp. 9163--9183, 2021, https://doi.org/10.1007/s00500-021-05877-0, opens an external URL in a new window.
• M. Pratama and C. Zain and E. Lughofer and E. Pardede and A.P.Rahayu, Scalable teacher forcing network for semi-supervised large scale data streams. Information Sciences, vol. 576, pp. 407--431, 2021, https://doi.org/10.1016/j.ins.2021.06.075, opens an external URL in a new window.

• P.V. De Campos Souza and E. Lughofer, An Advanced Interpretable Fuzzy Neural Network Model based on Uni-Nullneuron constructed from N-uninorms, Fuzzy Sets and Systems, opens an external URL in a new window, on-line and in press, https://doi.org/10.1016/j.fss.2020.11.019, opens an external URL in a new window, 2021

• J. de Jesús Rubio and E. Lughofer and J. Pieper and P. Cruz and D. Ivan Martinez and G. Ochoa and M. Antonio Islas and E. Garcia. Adapting H-infinity controller for the desired reference tracking of the sphere position in the maglev process. Information Sciences, vol. 569, pp. 669--686, 2021, https://doi.org/10.1016/j.ins.2021.05.018., opens an external URL in a new window

• E. Lughofer, R. Pollak, C. Feilmayr, M. Schatzl and S. Saminger-Platz, Prediction and Explanation Models for Hot Metal Temperature, Silicon Concentration and Cooling Capacity in Ironmaking Blast Furnaces, Steel Research International, opens an external URL in a new window, to appear, 2021

• A.C. Zavoianu, E. Lughofer, R. Pollak, C. Eitzinger, T. Radauer, A Soft-Computing Framework for Automated Optimization of Multiple Product Quality Criteria with Application to Micro-Fluidic Chip Production, Applied Soft Computing, articleID: 106827, 2020, https://doi.org/10.1016/j.asoc.2020.10682, opens an external URL in a new window

• P.V. De Campos Souza and E. Lughofer, Interpretable Hybrid Model in the Identification of Heart Sounds, Sensors, opens an external URL in a new window, vol. 20 (22), 2020, articleID: 6477

• E. Lughofer, A.C. Zavoianu, R. Pollak, M. Pratama, P. Meyer-Heye, H. Zörrer, C. Eitzinger and T. Radauer, On-line Anomaly Detection with Advanced Independent Component Analysis of Multi-Variate Residual Signals from Causal Relation Networks, Information Sciences, opens an external URL in a new window, vol. 537, pp. 425-451, 2020, https://doi.org/10.1016/j.ins.2020.06.034, opens an external URL in a new window

• P.V. De Campos Souza, H. Ponce and E. Lughofer, Evolving Fuzzy Neural Hydrocarbon Networks: A Model Based on Organic Compounds, Knowledge-Based Systems, opens an external URL in a new window, vol. 203, article nr: 106099, 2020, https://doi.org/10.1016/j.knosys.2020.106099, opens an external URL in a new window

• P.V. De Campos Souza, L.C. Bambirra Torres, G.R. Lacerda Silva, A.P. Braga, E. Lughofer, An Advanced Pruning Method in the Architecture of Extreme Learning Machines using L1-regularization and Bootstrapping, Electronics, opens an external URL in a new window, vol. 9(5), ID: 811, 2020, https://doi.org/10.3390/electronics9050811, opens an external URL in a new window

• S. Benmoussa, M. Djeziri, M. Sayed-Mouchaweh, E. Lughofer, Fault diagnosis and prognosis based on physical knowledge and reliability data: application to MOS Field-Effect Transistor, Microelectronics Reliability, opens an external URL in a new window, vol. 110, ID: 113682, 2020, https://doi.org/10.1016/j.microrel.2020.113682, opens an external URL in a new window

• E. Lughofer and R. Nikzad-Langerodi, Robust Generalized Fuzzy Systems Training from High-Dimensional Time-Series Data using Local Structure Preserving PLS, IEEE Transactions on Fuzzy Systems, opens an external URL in a new window, vol. 28 (11), 2020, DOI: 10.1109/TFUZZ.2019.2945535, opens an external URL in a new window

• W. Zellinger,  T. Grubinger, M. Zwick, E. Lughofer, H. Schöner, T. Natschläger, S. Saminger-Platz, Multi-Source Transfer Learning of Time Series in Cyclical Manufacturing, Journal of Intelligent Manufacturing, opens an external URL in a new window, vol. 31, pp. 777-787, 2020, https://doi.org/10.1007/s10845-019-01499-4, opens an external URL in a new window

• M. Ferdaus, M. Pratama, S. G. Anavatti, M. A. Garratt and E. Lughofer, PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles, Information Sciences, opens an external URL in a new window, vol. 512, pp. 481-505, 2020, https://doi.org/10.1016/j.ins.2019.10.0012, opens an external URL in a new window

• A. Ashfahani, M. Pratama, E. Lughofer, and Y.-S. Ong, DEVDAN: Deep Evolving Denoising Autoencoder, Neurocomputing, opens an external URL in a new window, vol. 390, pp. 297-314, 2020

• E. Lughofer, A.-C. Zavoianu, R. Pollak, M. Pratama, P. Meyer-Heye, H. Zörrer, C. Eitzinger, T. Radauer, Autonomous Supervision and Optimization of Product Quality in a Multi-Stage Manufacturing Process based on Self-Adaptive Prediction Models, Journal of Process Control, opens an external URL in a new window, vol 76, pp. 27-45, 2019, https://doi.org/10.1016/j.jprocont.2019.02.005, opens an external URL in a new window

• Igor Skrjanc, Jose Iglesias, Araceli Sanchis, Daniel Leite, Edwin Lughofer and Fernando Gomide, Evolving Fuzzy and Neuro-Fuzzy Approaches in Clustering, Regression, Identification, and Classification: A Survey, Information Sciences, opens an external URL in a new window, vol. 490, pp. 344-368, 2019, https://doi.org/10.1016/j.ins.2019.03.060, opens an external URL in a new window (cited 139 times, Google Scholar, "h")

• I. Skrjanc, S. Blazic, E. Lughofer and D. Dovzan, Inner Matrix Norms in evolving Cauchy Possibilistic Clustering for Classification and Regression from Data Streams, Information Sciences, opens an external URL in a new window, vol. 478, pp. 540-563, 2019, https://doi.org/10.1016/j.ins.2018.11.040, opens an external URL in a new window

• W. Zellinger, B.A. Moser, T. Grubinger, E. Lughofer, T. Natschläger and S. Saminger-Platz. Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment. Information Sciences, opens an external URL in a new window, vol. 483, pp. 174-191, 2019, https://doi.org/10.1016/j.ins.2019.01.025, opens an external URL in a new window (cited 43 times, Google Scholar)

• T. Weiler and E. Lughofer. Approximation of Incoherent Probabilities. International Journal of Approximate Reasoning, opens an external URL in a new window, vol. 105, pp. 342--355, 2019, https://doi.org/10.1016/j.ijar.2018.12.009, opens an external URL in a new window

• M. Pratama, W. Pedrycz and E. Lughofer. Online Tool Condition Monitoring Based on Parsimonious Ensemble+. , opens an external URL in a new windowIEEE Transactions on Cybernetics, opens an external URL in a new window, to appear, vol. 50 (2), pp. 664-677, DOI: 10.1109/TCYB.2018.2871120, opens an external URL in a new window

• Edwin Lughofer*, Mahardhika Pratama and Igor Skrjanc, Incremental Rule Splitting in Generalized Evolving Fuzzy Systems for Autonomous Drift Compensation, IEEE Transactions on Fuzzy Systems, opens an external URL in a new window, vol. 26(4), pp. 1854--1865, 2018, DOI: 10.1109/TFUZZ.2017.2753727 , opens an external URL in a new window(cited 61 times, Google Scholar, "h")

• J. Liu, E. Lughofer, X. Zeng and Z. Li. The Power of Visual Texture in Aesthetic Perception: an exploration of the predictability of perceived aesthetic emotions.
  Computational Intelligence and Neuroscience, opens an external URL in a new window,  Article ID 1812980, 2018, https://doi.org/10.1155/2018/1812980, opens an external URL in a new window

• Jose de Jesus Rubio, Edwin Lughofer, Jesus A. Meda Campana, Luis Alberto Paramo, Juan Francisco Novoa, Jaime Pacheco, Neural network updating via argument Kalman filter for modeling of Takagi-Sugeno fuzzy models, Journal of Intelligent and Fuzzy Systems, opens an external URL in a new window, vol. 35, no. 2, pp. 2585-2596, 2018, DOI: 10.3233/JIFS-18425, opens an external URL in a new window (cited 57 times, Google Scholar, "h")

• Ramin Nikzad-Langerodi*, Werner Zellinger, Edwin Lughofer and Susanne Saminger-Platz,
  Domain-Invariant Partial Least Squares Regression, Analytical Chemistry, opens an external URL in a new window, on-line and in press, 2018, DOI: 10.1021/acs.analchem.8b00498, opens an external URL in a new window

• Ramin Nikzad-Langerodi, Edwin Lughofer*, Carlos Cernuda, Thomas Reischer, Wolfgang Kantner, Marcin Pawliczek, Markus Brandstetter, Calibration Model Maintenance in Melamine Resin Production: Integrating Drift Detection, Smart Sample Selection and Model Adaptation, Analytica Chimica Acta, opens an external URL in a new window, vol. 1013, pp. 1—12, 2018, appeared  as FEATURED ARTICLE for Front Cover Issue https://doi.org/10.1016/j.aca.2018.02.003, opens an external URL in a new window

• Gabriel Kronberger*, Michael Kommenda, Edwin Lughofer, Susanne Saminger-Platz, Andreas Promberger, Falk Nickel, Stephan Winkler and Michael Affenzeller, Using Robust Generalized Fuzzy Modeling and Enhanced Symbolic Regression to Model Tribological Systems, Applied Soft Computing, opens an external URL in a new window,  vol. 69, pp. 610-624, 2018.

• Mahardhika Pratama, Witold Pedrycz and Edwin Lughofer, Evolving Ensemble Fuzzy Classifier, IEEE Transactions on Fuzzy Systems, opens an external URL in a new window, vol. 26 (5), pp. 2552-2567, 2018, DOI: 10.1109/TFUZZ.2018.2796099, opens an external URL in a new window (cited 66 times, Google Scholar, "h")

• Edwin Lughofer*, Alexandru-Ciprian Zavoianu, Robert Pollak, Mahardhika Pratama, Pauline Meyer-Heye, Helmut Zörrer, Christian Eitzinger, Julia Haim and Thomas Radauer, Self-Adaptive Evolving Forecast Models with Incremental PLS Space Updating for On-line Prediction of Micro-fluidic Chip Quality, Engineering Applications of Artificial Intelligence, opens an external URL in a new window, vol. 68, pp.131-151, 2018, https://doi.org/10.1016/j.engappai.2017.11.001, opens an external URL in a new window

• Edwin Lughofer*, Mahardhika Pratama, On-line Active Learning in Data Stream Regression using Uncertainty Sampling based on Evolving Generalized Fuzzy Models, IEEE Transactions on Fuzzy Systems, opens an external URL in a new window, vol. 26 (1), pp. 292--309, 2018, DOI: 10.1109/TFUZZ.2017.2654504, opens an external URL in a new window (cited 70 times, Google Scholar, "h")

• Mahardhika Pratama*, Edwin Lughofer, Plamen Angelov and Meng Joo Er.
  Parsimonious Random Vector Functional Link Network for Data Streams.
  Information Sciences, opens an external URL in a new window, vol. 430-431, pp. 519-537, 2018, https://doi.org/10.1016/j.ins.2017.11.050, opens an external URL in a new window

• Jianli Liu*, Edwin Lughofer and Xianyi Zeng, Toward Model Building for Visual Aesthetic Perception - SURVEY/POSITION Paper, Computational Intelligence and Neuroscience, opens an external URL in a new window, Article ID 1292801, 13 pages, 2018, https://doi.org/10.1155/2017/1292801, opens an external URL in a new window

• Jose de Jesus Rubio*, Edwin Lughofer, Plamen Angelov, Juan Francisco Novoa and Jesus A. Meda-Campana, A Novel Algorithm for the Modeling of Complex Processes, Kybernetika, opens an external URL in a new window, vol. 54 (1), pp. 79-95, 2018, DOI: , opens an external URL in a new window10.14736/kyb-2018-1-0079, opens an external URL in a new window

• Edwin Lughofer*, Roland Richter, Ulrich Neissl, Wolfgang Heidl, Christian Eitzinger, Thomas Radauer, Explaining Classifier Decisions Linguistically for Stimulating and Improving Operators Labeling Behavior, Information Sciences, opens an external URL in a new window, vol. 420, pp. 16-36, 2017, http://www.sciencedirect.com/science/article/pii/S0020025517308678, opens an external URL in a new window

• Edwin Lughofer*, On-line Active Learning: A New Paradigm to Improve Practical Useability of Data Stream Modeling Methods - SURVEY/POSITION Paper, Information Sciences, opens an external URL in a new window, vol. 415-416, pp. 356-376, 2017, https://doi.org/10.1016/j.ins.2017.06.038, opens an external URL in a new window (cited 51 times, Google Scholar, "h")

• Mahardhika Pratama, Eric Dimla, Chow Yin Lai, Edwin Lughofer*, Meng Joo Er, Metacognitive Learning Approach for Online Tool Condition Monitoring, Journal of Intelligent Manufacturing, opens an external URL in a new window, on-line and in press, 2017, https://doi.org/10.1007/s10845-017-1348-9, opens an external URL in a new window

• Ramin Nikzad-Langerodi, Edwin Lughofer*, Susanne Saminger-Platz, Thomas Zahel, Patrick Sagmeister, Christoph Herwig, Automatic Feed Phase Identification in Multivariate Process Profiles by Sequential Binary Classification, Analytica Chimica Acta, opens an external URL in a new window, vol. 982, pp. 48-61, 2017, https://doi.org/10.1016/j.aca.2017.05.03, opens an external URL in a new window

• Francisco Serdio, Edwin Lughofer*, Ciprian Zavoianu, Kurt Pichler, Markus Pichler, Thomas Buchegger, Hajrudin Efendic, Improved Fault Detection employing Hybrid Memetic Fuzzy Modeling and Adaptive Filters, Applied Soft Computing, opens an external URL in a new window, vol. 51, pp. 60-82, 2017, http://dx.doi.org/10.1016/j.asoc.2016.11.038, opens an external URL in a new window

• Choiru Zain*, Mahardhika Pratama, Edwin Lughofer, Sreenatha Anavatti, Evolving Type-2 Web News Mining, Applied Soft Computing, opens an external URL in a new window, vol. 54, pp. 200-220, 2017, http://dx.doi.org/10.1016/j.asoc.2016.11.034, opens an external URL in a new window (cited 45 times, Google Scholar, "h")

• Edwin Lughofer*, Stefan Kindermann, Mahardhika Pratama and Jose de Jesus Rubio. Top-Down Sparse Fuzzy Regression Modeling from Data with Improved Coverage, International Journal of Fuzzy Systems, opens an external URL in a new window, vol. 19 (5), pp. 1645--1658, 2017, doi:10.1007/s40815-016-0271-0, opens an external URL in a new window

• Mahardhika Pratama*, Edwin Lughofer, Meng Joo Er and Chee-Peng Lim, Data Driven Modeling based on Recurrent Interval-Valued Metacognitive Scaffolding Fuzzy Neural Network, Neurocomputing, opens an external URL in a new window, vol. 262, pp. 4-27,https://doi.org/10.1016/j.neucom.2016.10.093, opens an external URL in a new window, 2017

• Mahardhika Pratama*, Jie Lu, Edwin Lughofer, Guang Zhang and Meng Joo Er, Incremental Learning of Concept Drift Using Evolving Type-2 Recurrent Fuzzy Neural Network, IEEE Transactions on Fuzzy Systems,, opens an external URL in a new window vol. 25 (5), pp. 1175--1192, 2017, 10.1109/TFUZZ.2016.2599855, opens an external URL in a new window (cited 117 times, Google Scholar, "h")

• Mahardhika Pratama, Edwin Lughofer, Chee Peng Lim, Wenny Rahayu, Taram Dillon and Agus Budiyono, pClass+: A novel Evolving Semi-supervised Classifier, International Journal of Fuzzy Systems, opens an external URL in a new window, vol. 19 (3), pp. 863--880, 2017, DOI: 10.1007/s40815-016-0236-3, opens an external URL in a new window

• José de Jesús Rubio*, L. Zhang, E. Lughofer, P. Cruz, A. Alsaedi, T. Hayat. Modeling and control with neural networks for a magnetic levitation system. Neurocomputing, opens an external URL in a new window, vol. 227, pp. 113-121, 2016, http://dx.doi.org/10.1016/j.neucom.2016.09.101, opens an external URL in a new window (cited 52 times, Google Scholar, "h")

• Carlos Cernuda, Edwin Lughofer*, Helmut Klein, Clemens Forster, Marcin Pawliczek and Markus Brandstetter, Improved Quantification of Important Beer Quality Parameters based on Non-linear Calibration Methods applied to FT-MIR Spectra, Analytical and Bioanalytical Chemistry, opens an external URL in a new window (special issue on "Process Analytics" organized by Rudolf Kessler), vol. 409 (3), pp. 841-857, 2016, 10.1007/s00216-016-9785-4, opens an external URL in a new window

• Gerd Bramerdorfer*, Alexandru-Ciprian Zavoianu, Siegfried Silber, Edwin Lughofer, Wolfgang Amrhein, Possibilities for Speeding-Up the FE-Based Optimization of Electrical Machines - A Case Study, IEEE Transactions on Industrial Applications, opens an external URL in a new window, vol. 52 (6), pp. 4668-4677, 2016, 10.1109/TIA.2016.2587702, opens an external URL in a new window

• Edwin Lughofer*, Eva Weigl, Wolfgang Heidl, Christian Eitzinger and Thomas Radauer, Recognizing Input Space and Target Concept Drifts in Data Streams with Scarcely Labelled and Unlabelled Instances, Information Sciences, opens an external URL in a new window, vol. 355-356, pp. 127-151, 2016, doi:10.1016/j.ins.2016.03.034, opens an external URL in a new window (cited 44 times, Google Scholar, "h")

• Mahardhika Pratama* and Jie Lu and E. Lughofer and G. Zhang and Sreenatha Anavatti, Scaffolding Type-2 Classifier for Incremental Learning under Concept Drifts, NeuroComputing, opens an external URL in a new window, vol. 191, pp. 304-329, 2016, doi:10.1016/j.neucom.2016.01.049, opens an external URL in a new window (cited 81 times, Google Scholar, "h")

• Eva Weigl*, Wolfgang Heidl, Edwin Lughofer, Christian Eitzinger and Thomas Radauer, On Improving Performance of Surface Inspection Systems by On-line Active Learning and Flexible Classifier Updates, Machine Vision and Applications, opens an external URL in a new window, vol. 27 (1), pp. 103-127, 2016, doi: 10.1007/s00138-015-0731-9, opens an external URL in a new window

• Edwin Lughofer*, Carlos Cernuda, Stefan Kindermann and Mahardhika Pratama, Generalized Smart Evolving Fuzzy Systems, Evolving Systems, opens an external URL in a new window, vol. 6 (4), pp. 269-292, 2015, doi: 10.1007/s12530-015-9132-6, opens an external URL in a new window (cited 146, Google Scholar, "h").

• Edwin Lughofer* and Moamar Sayed-Mouchaweh, Autonomous Data Stream Clustering Implementing Split-and-Merge Techniques - Towards a Plug-and-Play Approach, Information Sciences, opens an external URL in a new window, vol. 204, pp. 54--79, 2015 (cited 108, "h", Google Scholar).

• Edwin Lughofer*, Eva Weigl, Wolfgang Heidl, Christian Eitzinger, Thomas Radauer, Integrating new Classes On the Fly in Evolving Fuzzy Classifier Designs and Its Application in Visual Inspection, Applied Soft Computing, opens an external URL in a new window, vol. 35, pp. 558-582, 2015, doi:10.1016/j.asoc.2015.06.038, opens an external URL in a new window (cited 39 times, Google Scholar, "h")

• Jianli Liu*, Edwin Lughofer and Xianyi Zeng, Aesthetic Perception of Visual Textures: A Holistic Exploration using Texture Analysis, Psychological Experiment and Perception Modeling, Frontiers of Computational Neuroscience, opens an external URL in a new window, vol. 9:134, pp. 1--14, 2015, http://dx.doi.org/10.3389/fncom.2015.00134, opens an external URL in a new window

• Carlos Cernuda, Edwin Lughofer*, Thomas Röder, Wolfgang Märzinger, Thomas Reischer, Marcin Pawliczek and Markus Brandstätter, Self-Adaptive Non-Linear Methods for Improved Multivariate Calibration in Chemical Processes, Lenzinger Berichte, opens an external URL in a new window, vol. 92, pp. 12--32, 2015

• Kurt Pichler*, Edwin Lughofer, Markus Pichler, Thomas Buchegger, Erich Peter Klement and Matthias Huschenbett, Fault detection in reciprocating compressor valves under varying load conditions, Mechanical Systems and Signal Processing, opens an external URL in a new window, vol. 70-71, pp. 104-119, 2016, doi:10.1016/j.ymssp.2015.09.005, opens an external URL in a new window (cited 70 times, Google Scholar, "h")

• Mahardhika Pratama*, Sreenatha Anavatti, Edwin Lughofer, C.P. Lim, An Incremental Meta-cognitive-based Scaffolding Fuzzy Neural Network, NeuroComputing, opens an external URL in a new window, vol. 171, pp. 89-105, 2016, doi:10.1016/j.neucom.2015.06.022, opens an external URL in a new window (cited 94 times, Google Scholar "h").

• Alexandru-Ciprian Zavoianu*, Edwin Lughofer, Werner Koppelstaetter, Günther Weidenholzer, Wolfgang Amrhein, Erich Peter Klement, Performance Comparison of Generational and Steady-State Asynchronous Multi-Objective Evolutionary Algorithms for Computationally-Intensive Problems, Knowledge-Based Systems, opens an external URL in a new window, vol. 87, pp. 47-60, 2015, doi:10.1016/j.knosys.2015.05.029, opens an external URL in a new window

• Jianli Liu*, Edwin Lughofer, Xianyi Zeng, Could Linear Model Bridge the Gap between Low-level Statistical Features and Aesthetic Emotions of Visual Textures?, NeuroComputing, opens an external URL in a new window, vol. 168 (30), pp. 947-960, 2015, doi:10.1016/j.neucom.2015.05.030, opens an external URL in a new window

• Francisco Serdio, Edwin Lughofer*, Kurt Pichler, Markus Pichler, Thomas Buchegger and Hajrudin Efendic, Fuzzy Fault Isolation using Gradient Information and Quality Criteria from System Identification Models, Information Sciences, opens an external URL in a new window, vol. 316, pp. 18-39, 2015, doi:10.1016/j.ins.2015.04.008, opens an external URL in a new window

• Moamar Sayed-Mouchaweh* and Edwin Lughofer, Decentralized Fault Diagnosis Approach without a Global Model for Fault Diagnosis of Discrete Event Systems, International Journal of Control, opens an external URL in a new window, vol. 88 (11), pp. 2228-2241, 2015, doi: 10.1080/00207179.2015.1039594, opens an external URL in a new window

• Mahardhika Pratama*, Sreenatha.G.Anavatti, Meng Joo Er and Edwin Lughofer, pClass: An Effective Classifier for Streaming Examples, IEEE Transactions on Fuzzy Systems, opens an external URL in a new window, vol. 23 (2), pp. 369-386, 2015, (cited 109, Google Scholar, "h"). doi: 10.1109/TFUZZ.2014.2312983, opens an external URL in a new window

• Kurt Pichler*, Edwin Lughofer, Markus Pichler, Thomas Buchegger, Erich Peter Klement and Mathias Huschenbett, Detecting cracks in reciprocating compressor valves using pattern recognition in the pV diagram, Pattern Analysis and Applications, opens an external URL in a new window, vol. 18 (2), pp. 461-472, 2015, doi: 10.1007/s10044-014-0431-5, opens an external URL in a new window

• Carlos Cernuda, Edwin Lughofer*, Georg Mayr. Thomas Röder and Peter Hintenaus and Wolfgang Märzinger and Jürgen Kasberger.Incremental and Decremental Active Learning for Optimized Self-Adaptive Calibration in Viscose Production, Chemometrics and Intelligent Laboratory Systems, opens an external URL in a new window, vol. 138, pp. 14-29, 2014, DOI: 10.1016/j.chemolab.2014.07.008, opens an external URL in a new window

• Ammar Shaker and Edwin Lughofer*. Self-Adaptive and Local Strategies for a Smooth Treatment of Drifts in Data Streams, Evolving Systems, opens an external URL in a new window, vol. 5 (4), pp. 239-257, 2014, doi: 10.1007/s12530-014-9108-y, opens an external URL in a new window (cited 63, Google Scholar, "h").

• Francisco Serdio, Edwin Lughofer*, Kurt Pichler, Thomas Buchegger, Markus Pichler and Hajrudin Efendic. Fault Detection in Multi-Sensor Networks based on Multivariate Time-Series Models and Orthogonal Transformations. Information Fusion, opens an external URL in a new window, vol. 20, pp. 272-291, 2014, http://dx.doi.org/10.1016/j.inffus.2014.03.006, opens an external URL in a new window (cited 94 times, Google Scholar, "h")

• Alexandru-Ciprian Zavoianu*, Edwin Lughofer, Gerd Bramerdorfer, Wolfgang Amrhein, Erich Peter Klement, DECMO2 - A Robust Hybrid and Adaptive Multi-Objective Evolutionary Algorithm, Soft Computing, opens an external URL in a new window, vol. 19 (12), pp. 3551-3569, 2015, doi: 10.1007/s00500-014-1308-7, opens an external URL in a new window (cited 56 times, Google Scholar, "h")

• Carlos Cernuda*, Edwin Lughofer, Peter Hintenaus and Wolfgang Märzinger, Enhanced Genetic Operators Design for Waveband Selection in Multivariate Calibration by NIR Spectroscopy, Journal of Chemometrics, opens an external URL in a new window, vol. 28 (3), pp. 123-136, 2014, DOI: 10.1002/cem.2583, opens an external URL in a new window

• Edwin Lughofer, On-line Assurance of Interpretability Criteria in Evolving Fuzzy Systems --- Achievements, New Concepts and Open Issues, Information Sciences, opens an external URL in a new window, vol. 251, pp. 22-46, 2013, http://dx.doi.org/10.1016/j.ins.2013.07.002, opens an external URL in a new window (cited 118 times, Google Scholar, "h")

• Mahardhika Pratama*, Sreenatha.G.Anavatti, Plamen Angelov and Edwin Lughofer, PANFIS: A Novel Incremental Learning Machine, IEEE Transactions on Neural Networks and Learning Systems, opens an external URL in a new window, vol. 25 (1), pp. 55-68, 2014, doi: 10.1109/TNNLS.2013.2271933, opens an external URL in a new window (cited 227 times, Google Scholar, "h")

• Francisco Serdio, Edwin Lughofer*, Kurt Pichler, Thomas Buchegger and Hajrudin Efendic, Residual-Based Fault Detection using Soft Computing Techniques for Condition Monitoring at Rolling Mills, Information Sciences, opens an external URL in a new window, vol. 259, pp. 304-320, 2014, doi: dx.doi.org/10.1016/j.ins.2013.06.045, opens an external URL in a new window (cited 95 times, Google Scholar, "h")

• Alexandru-Ciprian Zavoianu, Gerd Bramerdorfer, Edwin Lughofer*, Siegfried Silber, Wolfgang Amrhein, Erich Peter Klement, Hybridization of Multi-Objective Evolutionary Algorithms and Artificial Neural Networks for Optimizing the Performance of Electrical Drives, Engineering Applications of Artificial Intelligence, opens an external URL in a new window, vol. 26 (8), pp. 1781-1794, 2013, http://dx.doi.org/10.1016/j.engappai.2013.06.002, opens an external URL in a new window (cited 80 times, Google Scholar, "h")

• Mahardhika Pratama*, Sreenatha.G.Anavatti and Edwin Lughofer, GENEFIS: Towards an Effective Localist Network, IEEE Transactions on Fuzzy Systems, opens an external URL in a new window, vol. 22 (3), pp. 547-562, 2014, doi: 10.1109/TFUZZ.2013.2264938, opens an external URL in a new window (cited 149 times, Google Scholar, "h")

• Carlos Cernuda, Edwin Lughofer*, Peter Hintenaus, Wolfgang Märzinger, Thomas Reischer, Marcin Pawlicek and Juergen Kasberger, Hybrid Adaptive Calibration Methods and Ensemble Strategy for Prediction of Cloud Point in Melamine Resin Production, Chemometrics and Intelligent Laboratory Systems, opens an external URL in a new window, vol. 126, pp. 60-75, 2013, http://dx.doi.org/10.1016/j.chemolab.2013.05.001, opens an external URL in a new window

• Edwin Lughofer* and Oliver Buchtala, Reliable All-Pairs Evolving Fuzzy Classifiers, IEEE Transactions on Fuzzy Systems, opens an external URL in a new window, vol. 21 (4), pp. 625-641, 2013. doi: http://dx.doi.org/10.1109/TFUZZ.2012.2226892, opens an external URL in a new window (cited 83 times, Google Scholar, "h")

• Wolfgang Heidl*, Stefan Thumfart, Edwin Lughofer, Christian Eitzinger and Erich Peter Klement, Machine Learning Based Analysis of Gender Differences in Visual Inspection Decision Making, Information Sciences, opens an external URL in a new window, vol. 224, pp. 62-76, 2013, doi: http://dx.doi.org/10.1016/j.ins.2012.09.054, opens an external URL in a new window.

• Mahardhika Pratama, M.J. Er, X. Li, Richard J. Oentaryo, Edwin Lughofer and Imam Arifin, Data Driven Modeling Based on Dynamic Parsimonious Fuzzy Neural Network, NeuroComputing, opens an external URL in a new window, vol. 110, pp. 18-28, 2013, http://dx.doi.org/10.1016/j.neucom.2012.11.013, opens an external URL in a new window (cited 68, Google Scholar, "h").

• Edwin Lughofer, Single-Pass Active Learning with Conflict and Ignorance, Evolving Systems, opens an external URL in a new window, vol. 3 (4), pp. 251-271, 2012, doi: 10.1007/s12530-012-9060-7, opens an external URL in a new window (cited 108 times, Google Scholar, "h")

• Carlos Cernuda, Edwin Lughofer*, Lisbeth Suppan, Thomas  Röder, Roman Schmuck, Peter Hintenaus, Wolfgang Märzinger, Jürgen  Kasberger, Evolving Chemometric Models for Predicting Dynamic Process Parameters in Viscose Production, Analytica Chimica Acta, opens an external URL in a new window, vol. 725, pp. 22-38, 2012,
  http://dx.doi.org/10.1016/j.aca.2012.03.012, opens an external URL in a new window

• Edwin Lughofer, A Dynamic Split-and-Merge Approach for Evolving Cluster Models, Evolving Systems, opens an external URL in a new window (special issue on 'Dynamic Clustering'), vol. 3 (3), pp. 135-151, 2012, DOI: 10.1007/s12530-012-9046-5, opens an external URL in a new window (cited 41 times, Google Scholar, "h")

• Edwin Lughofer, Hybrid Active Learning (HAL) for Reducing the Annotation Effort of Operators in Classification Systems, Pattern Recognition, opens an external URL in a new window, vol. 45 (2), pp. 884-896, 2012, DOI: , opens an external URL in a new window10.1016/j.patcog.2011.08.009, opens an external URL in a new window (cited 96 times, Google Scholar, "h")

• Carlos Cernuda, Edwin Lughofer*, Wolfgang Maerzinger and Juergen Kasberger, NIR-based Quantification of Process Parameters in Polyetheracrylat (PEA) Production using Flexible Non-linear Fuzzy Systems, Chemometrics and Intelligent Laboratory Systems, opens an external URL in a new window, vol. 109 (1), pp. 22-33, 2011,
  DOI: 10.1016/j.chemolab.2011.07.004, opens an external URL in a new window (cited 26, Google Scholar).

• Edwin Lughofer*, Bogdan Trawinski, Krzysztof Trawinski, Olgierd Kempa, Tadeusz Lasota, On Employing Fuzzy Modeling Algorithms for the Valuation of Residential Premises, Information Sciences, opens an external URL in a new window, vol. 181 (23), pp. 5123--5142, 2011, DOI: 10.1016/j.ins.2011.07.012, opens an external URL in a new window, (cited 61 times, Google Scholar, "h")

• Edwin Lughofer*, Jean-Luc Bouchot and Ammar Shaker. On-line Elimination of Local Redundancies in Evolving Fuzzy Systems. Evolving Systems, opens an external URL in a new window, vol. 2 (3), pp. 165--187, 2011, DOI: 10.1007/s12530-011-9032-3, opens an external URL in a new window. (cited 104 times, Google Scholar, "h")

• Edwin Lughofer*, Vicente Macian, Carlos Guardiola and Erich Peter Klement, Identifying Static and Dynamic Prediction Models for NOx Emissions with Evolving Fuzzy Systems, Applied Soft Computing, opens an external URL in a new window, vol. 11(2), pp. 2487-2500, 2011, doi:10.1016/j.asoc.2010.10.004, opens an external URL in a new window (cited 61 times, Google Scholar, "h")

• Edwin Lughofer, On-line Incremental Feature Weighting in Evolving Fuzzy Classifiers, Fuzzy Sets and Systems, opens an external URL in a new window, vol 163 (1), pp. 1-23, 2011, doi:10.1016/j.fss.2010.08.012, opens an external URL in a new window (cited 76 times, Google Scholar, "h")

• Edwin Lughofer* and Plamen Angelov, Handling Drifts and Shifts in On-Line Data Streams with Evolving Fuzzy Systems, Applied Soft Computing, opens an external URL in a new window, vol. 11(2), pp. 2057-2068, 2011, doi:10.1016/j.asoc.2010.07.003, opens an external URL in a new window (cited 171 times, Google Scholar, "h")

• Edwin Lughofer*, Stefan Kindermann, SparseFIS: Data-Driven Learning of Fuzzy Systems with Sparsity Contraints, IEEE Transactions on Fuzzy Systems, opens an external URL in a new window, vol. 18 (2), pp. 396-411, 2010, doi:10.1109/TFUZZ.2010.2042960, opens an external URL in a new window (cited 96 times, Google Scholar, "h")

• Werner Groissboeck, Edwin Lughofer*, Stefan Thumfart, Associating Visual Textures with Human Perceptions Using Genetic Algorithms, Information Sciences, opens an external URL in a new window, vol. 180 (11), pp. 2065-2084, 2010, doi:10.1016/j.ins.2010.01.035, opens an external URL in a new window (cited 40 times, Google Scholar)

• Stefan Thumfart*, Richard Jacobs, Edwin Lughofer, Christian Eitzinger, Frans Cornelissen, Werner Groissboeck, Roland Richter, Modelling Human Aesthetic Perception of Visual Textures, ACM Transactions on Applied Perception, opens an external URL in a new window, vol. 8 (4), 2011, , opens an external URL in a new windowDOI: , opens an external URL in a new window10.1145/2043603.2043609, opens an external URL

• Edwin Lughofer, On-line Evolving Image Classifiers and Their Application to Surface Inspection, Image and Vision Computing, opens an external URL in a new window(special issue of on-line pattern recognition and machine learning techniques for computer vision), Vol. 28 (7), pp. 1065-1079, 2010,DOI: 10.1016/j.imavis.2009.07.002, opens an external URL in a new window (cited 41 times, Google Scholar, "h")

• Edwin Lughofer*, James E. Smith, Muhammad A. Tahir, Praminda Caleb-Solly, Christian Eitzinger, Davy Sannen and Marnix Nuttin, Human-Machine Interaction Issues in Quality Control Based on On-Line Image Classification, IEEE Transactions on Systems, Man and Cybernetics, part A: Systems and Humans, opens an external URL in a new window, 2009, vol. 39 (5), pp. 960-971, DOI: 10.1109/TSMCA.2009.2025025, opens an external URL in a new window (cited 51 times, Google Scholar, "h")

• Davy Sannen, Edwin Lughofer* and Hendrik van Brussel, Towards Incremental Classifier Fusion, Intelligent Data Analysis, opens an external URL in a new window, Vol. 14 (1), pp. 3-30, 2010, DOI 10.3233/IDA-2009-0406, opens an external URL in a new window

• Christian Eitzinger*, Wolfgang Heidl, Edwin Lughofer, Stefan Raiser, James E. Smith, Muhammad A. Tahir, Davy Sannen and Hendrik van Brussel, Assessment of the Influence of Adaptive Components in Trainable Surface Inspection Systems, Machine Vision and Application, opens an external URL in a new windows, opens an external URL in a new window, Vol. 21 (5), pp. 613-626, 2010, DOI 10.1007/s00138-009-0211-1, opens an external URL in a new window (cited 42 times, Google Scholar, "h")

• Stefan Raiser, Edwin Lughofer*, Christian Eitzinger and James E. Smith, Impact of Object Extraction Methods on Classification Performance in Surface Inspection Systems,Machine Vision and Applications,, opens an external URL in a new window vol. 21(5), pp. 627-641, 2010, DOI: 10.1007/s00138-009-0205-z, opens an external URL in a new window

• Plamen Angelov, Edwin Lughofer*, Xiaowei Zhou. Evolving Fuzzy Classifiers using Different Model Architectures, Fuzzy Sets and Systems, opens an external URL in a new window, vol.159 (23), pp. 3160-3182, 2008, doi:10.1016/j.fss.2008.06.019, opens an external URL in a new window (cited 208 times, Google Scholar, "h")

• Edwin Lughofer. FLEXFIS: A Robust Incremental Learning Approach for Evolving TS Fuzzy Models, IEEE Transactions on Fuzzy Systems, opens an external URL in a new window, vol. 16 (6), pp. 1393-1410, 2008, doi 10.1109/TFUZZ.2008.925908, opens an external URL in a new window (cited 339 times, Google Scholar, "h")

• Edwin Lughofer. Extensions of Vector Quantization for Incremental Clustering. Pattern Recognition, opens an external URL in a new window, vol. 41(3), pp. 995-1011, 2008, doi:10.1016/j.patcog.2007.07.019, opens an external URL in a new window (cited 177 times, Google Scholar, "h")

• Edwin Lughofer* and Carlos Guardiola. On-Line Fault Detection with Data-Driven Evolving Fuzzy Models. Control and Intelligent Systems, opens an external URL in a new window, Vol. 36 (4), opens an external URL in a new window, pp. 307-317, 2008 (cited 25 times, Google Scholar) 

• Plamen Angelov* and Edwin Lughofer* , Data-Driven Evolving Fuzzy Systems using eTS and FLEXFIS: Comparative Analysis.International Journal of General Systems, opens an external URL in a new window, vol. 37(01), pp. 45 - 67, 2008

• Plamen Angelov, Veniero Giglio, Carlos Guardiola, Edwin Lughofer*, and Jose Manuel Lujan. An approach to model-based fault detection in industrial measurement systems with application to engine test benches. Measurement Science and Technology, opens an external URL in a new window, Vol. 17, pp.1809-1818, 2006  (cited 56 times, Google Scholar, "h").

* corresponding author(s)

• M.-Y. Chen and E. Lughofer and J. de J. Rubio and Y.J. Wu. Editorial: Anticipatory Systems: Humans Meet Artificial Intelligence. Frontiers of Psychology, vol. 12: 721879, 2021.
• M.-Y. Chen, H.-S. Chiang, E. Lughofer and E. Egrioglu, Deep learning: emerging trends, applications and research challenges, Soft Computing, opens an external URL in a new window, vol. 24, pp. 7835-7838, 2020
• Mu-Yen Chen, , opens an external URL in a new windowChien-Hsiang Liao , , opens an external URL in a new windowEdwin Lughofer, , opens an external URL in a new windowErol Egrioglu, opens an external URL in a new window, Informetrics on Social Network Mining: research, policy and practice challenges, Library Hi Tech, vol. 38 (2), 2020
• Igor Skrjanc*, Jose Iglesias, Araceli Sanchis, Daniel Leite, Edwin Lughofer and Fernando Gomide, Evolving Fuzzy and Neuro-Fuzzy Approaches in Clustering, Regression, Identification, and Classification: A Survey, Information Sciences, opens an external URL in a new window, vol. 490, pp. 344-368, 2019.
• J.J. Rubio*, Y. Pan, E. Lughofer, M.-Y. Chen and J. Qiu, Editorial: Fast Learning of Neural Networks with Application to Big Data Processes, Neurocomputing, opens an external URL in a new window, to appear, 2019.
• Mahardhika Pratama*, Edwin Lughofer, Suresh Sundaram, Moamar Sayed-Mouchaweh, Igor Skrjanc and Fahed Mostafa, Editorial: Special issue on Advanced Soft Computing for Prognostic Health Management, Applied Soft Computing, opens an external URL in a new window, vol. 72, pp. 552-554, 2018, https://doi.org/10.1016/j.asoc.2018.10.001, opens an external URL in a new window
• Mu-Yen Chen*, Edwin Lughofer and Robi Polikar. Big Data and Situation-Aware Technology for Smarter Healthcare. Journal of Medical and Biological Engineering, opens an external URL in a new window, vol. 38(6), pp. 845-846, 2018, https://doi.org/10.1007/s40846-018-0452-4, opens an external URL in a new window
• Mu-Yen Chen* and Edwin Lughofer, Editorial: Data Stream Mining and Soft Computing Applications, Applied Soft Computing, opens an external URL in a new window, vol. 68, pp. 667--668, 2018. https://doi.org/10.1016/j.asoc.2018.05.020, opens an external URL in a new window.
• Mu-Yen Chen and Edwin Lughofer, Editorial: Data Stream Mining and Soft Computing Applications, Applied Soft Computing, opens an external URL in a new window, to appear, 2018.
• Edwin Lughofer*, On-line Active Learning: A New Paradigm to Improve Practical Useability of Data Stream Modeling Methods - SURVEY/POSITION Paper, Information Sciences, opens an external URL in a new window, vol. 415-416, pp. 356-376, 2017, https://doi.org/10.1016/j.ins.2017.06.038, opens an external URL in a new window
• Mu-Yen Chen, Edwin Lughofer, N.Y. Yen and C.-C. Chen, Guest editorial: Human behavior analysis for library and information science. Library Hi Tech, opens an external URL in a new window, Vol. 35 Issue: 4, pp.442-444, 2017, https://doi.org/10.1108/LHT-10-2017-0213., opens an external URL in a new window
• Mahardhika Pratama, Edwin Lughofer, Dianhui Wang, Online Real-Time Learning Strategies for Data Streams, Neurocomputing, opens an external URL in a new window, vol. 262, pp. 1-3, 2017, https://doi.org/10.1016/j.neucom.2017.05.078, opens an external URL in a new window
• A. Fleury and E. Lughofer and M. Sayed-Mouchaweh, Editorial of the special issue: adaptive and intelligent systems (AIS) for learning, control and optimization in dynamic environments, Evolving System, opens an external URL in a new windows, vol. 8 (4), pp 317–-319, 2017, DOI: 10.1007/s12530-017-9179-7, opens an external URL in a new window
• Premyszlaw Kazienko, Edwin Lughofer and Bogdan Trawinski, Editorial on the Special Issue Hybrid and Ensemble Techniques in Soft Computing: Recent Advances and Emerging Trends, opens an external URL in a new window, Soft Computing, opens an external URL in a new window, vol. 19 (12), pp. 3353-3355, 2015
• Edwin Lughofer and Moamar Sayed-Mouchaweh, Adaptive and On-line Learning in Non-Stationary Environments, Evolving Systems, opens an external URL in a new window, vol. 6 (2), pp. 75-77, 2015, DOI: 10.1007/s12530-015-9128-2, opens an external URL in a new window
• Fernando Gomide and Edwin Lughofer. Recent Advances on Evolving Intelligent Systems and Applications, opens an external URL in a new window, Evolving Systems, opens an external URL in a new window, vol. 5 (4), pp. 217-218, 2014
• Edwin Lughofer, On-line Assurance of Interpretability Criteria in Evolving Fuzzy Systems --- Achievements, New Concepts and Open Issues, Information Sciences, opens an external URL in a new window, vol. 251, 22-46, 2013, http://dx.doi.org/10.1016/j.ins.2013.07.002, opens an external URL in a new window
• Edwin Lughofer, Human-Inspired Evolving Machines - The Next Generation of Evolving Intelligent Systems?, IEEE SMC newsletter, vol. 36, opens an external URL in a new window, 2011.
• M.-Y. Chen, E. Lughofer, K. Sakamura. Information Fusion in Smart Living Technology Innovations, opens an external URL in a new window. Information Fusion, opens an external URL in a new window, vol. 21, pp. 1-2, 2015
• Abdelhamid Bouchachia, Edwin Lughofer and Moamar Sayed-Mouchaweh, Editorial: Evolving Soft Computing Techniques and Applications, opens an external URL in a new window, Applied Soft Computing, opens an external URL in a new window, vol. 14, part B, pp. 141-143, 2014
• Abelhamid Bouchachia, Edwin Lughofer and Daniel Sanchez, Editorial of the special issue: Online Fuzzy Machine Learning and Data Mining, Information Sciences, opens an external URL in a new window, vol. 220, pp. 1-4, 2013, http://dx.doi.org/10.1016/j.ins.2012.10.005, opens an external URL in a new window
• Przemyslaw Kazienko, Edwin Lughofer and Bogdan Trawinksi, Editorial of the Special Issue on "Hybrid and Ensemble Methods in Machine Learning", opens an external URL in a new window, Journal of Universal Computer Science, opens an external URL in a new window, vol. 19 (4), pp. 457-461, 2013
• Mu-Yen Chen and Edwin Lughofer, Editorial of the special issue: Smart Space Technology Innovations, opens an external URL in a new window, Library Hi Tech, opens an external URL in a new window, vol. 31 (2), pp. 1-3, 201

Download the full list of my conference papers here, opens an external URL in a new window.

• P. V. de C. Souza and E. Lughofer and A. J. Guimaraes. Evolving Fuzzy Neural Network Based on Uni-Nullneuron to Identify Auction Fraud. Proceedings of 12^th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), 2021 (won the Best Paper Award).
• P. V. de C. Souza and E. Lughofer and A. J. Guimaraes. Regularized neuro-fuzzy AI model to aid score management in online distance learning forums. Proceedings of the International Conference on Fuzzy Systems (FUZZ-IEEE), 2021.
• A. Ashfahani, M. Pratama, E. Lughofer and E. Y. Kien Yee. Autonomous Deep Quality Monitoring in Streaming Environments. Proceedings of the International Joint Conference on Neural Networks, virtual event, to appear, 2021.
• P. V. de Campos Souza, Y.-K. Wang and E. Lughofer. Knowledge extraction about patients surviving breast cancer treatment through an autonomous fuzzy neural network. Proceedings of the International Conference on Fuzzy Systems (FUZZ-IEEE), 2020.
• A. J. Guimaraes, P. V. de Campos Souza, E. Lughofer. Hybrid Model for Parkinson’s Disease Prediction. Proceedings of the International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU) 2020, Glasgow, Scotland, 2020.
• G. Andonovski, O. Sipele, J.A. Iglesias, A. Sanchis, E. Lughofer and I. Škrjanc. Detection of driver maneuvers using evolving fuzzy cloud-based system. Proceedings of the IEEE SSCI Conference, Melbourne, Australia, 2020.
• E. Lughofer, Variants of Recursive Consequent Learning in Evolving Neuro-Fuzzy Systems, Proceedings of the IEEE Evolving and Adaptive Intelligent Systems EAIS) Conference 2020, Bari, Italy.
• E. Lughofer and M. Pratama, On-line Sequential Ensembling of Fuzzy Systems, Proceedings of the IEEE Evolving and Adaptive Intelligent Systems EAIS) Conference 2020, Bari, Italy.
• C. Zain, A. Ashfahani, M. Pratama, E. Lughofer and E. Pardede, Scalable Teacher-Forcing Networks under Spark Environments for Large-Scale Streaming Problems, Proceedings of the IEEE Evolving and Adaptive Intelligent Systems EAIS) Conference 2020, Bari, Italy.
• E. Lughofer, M. Pratama, C. Eitzinger and T. Radauer. Dynamic Anomaly Detection based on Recursive Independent Component Analysis of Multi-Variate Residual Signals. Proceedings of the 33rd annual European Simulation and Modelling Conference, opens an external URL in a new window, Palma de Mallorca, Spain, 2019.
• M. Pratama, M. de Carvalho, R. Xie, E. Lughofer and J. Lu. ATL: Autonomous Knowledge Transfer from Many Streaming Processes. Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM) 2019, opens an external URL in a new window, Bejing, China, 2019.
• A. Baghbanpourasl, E. Lughofer, P. Meyer-Heye, H. Zörrer and C. Eitzinger. Virtual Quality Control using bidirectional LSTM networks and gradient boosting. Proceedings of the IEEE International Conference on Industrial Informatics (INDIN) 2019, opens an external URL in a new window, Helsinki, Finland, 2019.
• W. Zellinger, B. Moser, M. Zwick, E. Lughofer, T. Natschläger, and S. Saminger-Platz. Moment distances for comparing high-entropy distributions with application in domain adaptation [link], opens an external URL in a new window Proceedings of the International Conference on Computational Statistics (COMPSTAT, opens an external URL in a new window), 2018
• A.-C. Zavoianu, E. Lughofer, S. Saminger-Platz, W. Amrhein, Two Enhancements for Improving the Convergence  Speed of aRobust Multi-Objective Coevolutionary Algorithm, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) 2018, opens an external URL in a new window, Kyoto, Japan
• E. Lughofer, R. Pollak, A.C. Zavoianu, M. Pratama, P. Meyer-Heye, H. Zörrer, C. Eitzinger, J. Lehner and T. Radauer, Evolving Time-Series Based Prediction Models for Quality Criteria in a Multi-Stage Production Process. Proceedings of the IEEE Evolving and Adaptive Intelligent Systems (EAIS) Conference,, opens an external URL in a new window to appear, 2018, Rhodos, Greece.
• C. Zain, M. Pratama, E. Lughofer, M. Ferdaus and Q. Cai. Big Data Analytics based on PANFIS MapReduce. Proceedings of the INNS Big Data Conference, opens an external URL in a new window, to appear, 2018, Bali, Indonesia.
• A.C. Zavoianu, E. Lughofer, R. Pollak, P. Meyer-Heye, C. Eitzinger, T. Radauer, Multi-Objective Knowledge-Based Strategy for Process Parameter Optimization in Micro-Fluidic Chip Production, Proceedings of the SSCI 2017 Conference (CIES workshop), Honolulu, Hawaii, to appear, 2017
• E. Lughofer, R. Pollak, A.C. Zavoianu, P. Meyer-Heye, H. Zörrer, C. Eitzinger, J. Haim, T. Radauer, Self-Adaptive Time-Series Based Forecast Models for Predicting Quality Criteria in Microfluidics Chip Production, Proceedings of the IEEE Cybernetics Conference 2017, opens an external URL in a new window, Exeter, U.K., 2017, to appear.
• E. Lughofer, M. Pratama and I. Skrjanc, Incremental Rule Splitting in Generalized Evolving Fuzzy Regression Models, Proceedings of the IEEE Evolving and Adaptive Intelligent Systems Conference (IEEE EAIS 2017), Ljubljana, 2017.
• W. Zellinger, E. Lughofer, S. Saminger-Platz, T. Grubinger and T. Natschläger.
  Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning. Proceedings of the 5th ICLR Conference, Toulon, to appear, 2017.
• R. Nikzad Langerodi, E. Lughofer, T. Reischer, W. Kantner, M. Pawliczek and M. Brandstetter, Improved drift handling in melamine resin production by ensemble partial least squares and incremental Page-Hinkley testing, Proceedings of the EuroAnalysis 2017 Conference, opens an external URL in a new window, Stockholm, Sweden, to appear.
• M. Pratama, P.P. Angelov, J. Lu, E. Lughofer, M. Seera and Chee Peng Lim. A Randomized Neural Network for Data Streams.Proceedings of the International Joint Conference on Neural Networks (IJCNN 2017), opens an external URL in a new window, to appear, Anchorage, Alaska, U.S.A.
• G. Andonovski, E. Lughofer and I. Skrjanc. A Comparison of RECCO and FCPFC Controller on Nonlinear Chemical Reactor.  The 36th IASTED International Conference on Modelling, Identification and Control, opens an external URL in a new window, Innsbruck, Austria, 2017, to appear.
• R. Nikzad-Langerodi, E. Lughofer, T. Zahel, P. Sagmeister, C. Herwig.  A novel framework for automated feed phase identification. Proceedings of the 4th European Conference on Process Analytics and Control Technology (EuroPACT) 2017, opens an external URL in a new window, Potsdam, to appear.
• A.C. Zavoianu, G. Bramerdorfer, E. Lughofer and S. Saminger-Platz, Multi-Objective Topology Optimization of Electrical Machine Designs using Evolutionary Algorithms with Discrete and Real Encodings, Proceedings of the EuroCAST Conference, opens an external URL in a new window 2017, Las Palmas de Gran Canaria, Spain, to appear
• E. Lughofer, G. Kronberger, M. Kommenda, S. Saminger-Platz, A. Promberger, F. Nickel, S. Winkler and M. Affenzeller. Robust Fuzzy Modeling and Symbolic Regression for Establishing Accurate and Interpretable Prediction Models in Supervising Tribological Systems, Proceedings of the 8th International Conference on Fuzzy Computation Theory and Applications (FCTA) 2016, opens an external URL in a new window, pp. 51--63, 2016.
• E. Weigl, A. Walch, U. Neissl, P. Meyer-Heye, W. Heidl, C. Eitzinger, T. Radauer and E. Lughofer.  MapView: Graphical Data Representation for Active Learning, Proceedings of the iKnow Conference, opens an external URL in a new window 2016, Graz, Austria, 2016.
• E. Lughofer, R. Richter, U. Neissl, W. Heidl, C. Eitzinger and T. Radauer, Advanced Linguistic Explanations of Classifier Decisions for Users' Annotation Support, Proceedings of the IEEE Intelligent Systems Conference 2016, opens an external URL in a new window, Sofia, Bulgaria, 2016, pp. 421—432
• F. Serdio and E. Lughofer, A Fault Detection and Isolation Framework for Repeatable and Comparable Experimentation, opens an external URL in a new window, Proceedings of the PHM Conference/Society, Bilbao, on-line and in press, 2016
• M. Pratama, E. Lughofer, M.J. Err, W. Rahayu and T. Dillon, Evolving Type-2 Recurrent Fuzzy Neural Network, Proceedings of the IJCNN 2016 conference (under the scope of the WCCI 2016 conference, opens an external URL in a new window), Vancouver, Canada, 2016, to appear