Google Scholar: https://scholar.google.be/citations?user=sOSxU7sAAAAJ&hl=en
ResearchGate: https://www.researchgate.net/profile/Siamak_Mehrkanoon
The following listed publications are outdated. Please consult Google Scholar for a more comprehensive list.
Sheng Kuang, Sheng Kuang, Henry C. Woodruff, Renee Granzier c, Thiemo J.A. van Nijnatten, Marc B.I. Lobbes, Marjolein L. Smidt, Philippe Lambin, S. Mehrkanoon, “MSCDA: Multi-level semantic-guided contrast improves unsupervised domain adaptation for breast MRI segmentation in small datasets”, Neural Networks, vol. 165, pp. 119-134, 2023 [pdf]
Onur Bilgin, Thomas Vergutz, S. Mehrkanoon, “GCN-FFNN: A two-stream deep model for learning solution to partial differential equations”, Neurocomputing, vol. 511, pp. 131-141, 2022 [pdf]
K van der Heijden, S. Mehrkanoon, “Goal-driven, neurobiological-inspired convolutional neural network models of human spatial hearing ”, Neurocomputing, vol. 470, pp. 432-442, 2022 [pdf]
D. Vanpoucke, M. Delgove, J. Stouten, J. Noordijk, N. De Vos, K. Matthysen, G. Deroover, S. Mehrkanoon, K. Bernaerts , “A machine learning approach for the design of hyperbranched polymeric dispersing agents based on aliphatic polyesters for radiation‐curable inks ”, Polymer International , vol. 470, pp. 432-442, 2022[pdf]
K. Trebing, T. Staǹczyk, S. Mehrkanoon , “Smaat-unet: Precipitation nowcasting using a small attention-unet architecture ”, Pattern Recognition Letters, vol. 145, pp. 178-186, 2021. [GitHub]
JG Fernández, S. Mehrkanoon , “Broad-UNet: Multi-scale feature learning for nowcasting tasks ”, Neural Networks 144, 419-427, 2021 [GitHub]
D. Vanpoucke, O. Knippenberg, K. Hermans, K. Bernaerts, S Mehrkanoon , “ Small data materials design with machine learning: When the average model knows best ”, Journal of Applied Physics , 128, 054901-12 , 2020 [pdf]
S. Mehrkanoon, “Deep shared representation learning for weather elements forecasting ”, Knowledge-Based Systems, Vol 179, pp. 120-128,Sept 2019[pdf][dataset]
S. Mehrkanoon, “ cross-domain neural kernel networks ”, Pattern recognition letter, Vol 125, pp. 474-480, July 2019 [pdf].
S. Mehrkanoon, “ Deep neural kernel blocks ”, Neural Networks, Vol 116, pp. 46-55, August 2019 [pdf].
S. Mehrkanoon, J.A.K. Suykens, “Deep hybrid neural-kernel networks using random Fourier features”, Neurocomputing, Vol.298, pp.46-54, July 2018 [PDF].
S. Mehrkanoon, X. Huang, J.A.K. Suykens, “Indefinite Kernel Spectral Learning”, Pattern Recognition, Vol 78, June 2018, Pages 144-153 [PDF].
S. Mehrkanoon, J.A.K. Suykens, “Regularized Semi-Paired Kernel CCA for Domain Adaptation”, IEEE Trans. Neural Netw. Learning Syst, Vol 29, pp.3199-3213, 2018. [PDF]
Y. Feng, Y. Yang, X. Huang, S. Mehrkanoon, J.A.K. Suykens, “Robust Support Vector Machines for Classification with Nonconvex and Smooth Losses,” Neural-Computation, Vol. 28, no. 6, pp. 1217-1247, 2016. [PDF]
S. Mehrkanoon, Yuri A.W. Shardt, J. A. K. Suykens, Steven X. Ding, “Estimating the Unknown Time Delay in Chemical Processes,” Engineering Applications of Artificial Intelligence, Vol. 55, pp. 2190, 2016. [PDF]
S. Mehrkanoon, O. M. Agudelo, J. A. K. Suykens, “Incremental multi-class semi-supervised clustering regularized by Kalman filtering,” Neural Networks, Vol 71, pp. 88-104, 2015. [PDF]
S. Mehrkanoon, J. A. K. Suykens, “Learning solutions to partial differential equations using LS-SVM,” Neurocomputing, Vol. 159, pp. 105-116, 2015. [PDF]
R. Mall, S. Mehrkanoon, J. A. K. Suykens, “Identifying intervals for hierarchical clustering using the Gershgorin circle theorem,” Pattern Recognition Letters, Vol 55, pp. 1-7,2015. [PDF]
S. Mehrkanoon, C. Alzate, R. Mall, R. Langone, J. A. K. Suykens, “Multiclass semi-supervised learning based upon kernel spectral clustering,” IEEE Trans. Neural Netw. Learning Syst, Vol. 26, pp. 720-733, 2015. [PDF]
S. Mehrkanoon, X. Huang, J. A. K. Suykens, “Non-parallel support vector classifiers with different loss functions,” Neurocomputing, 143(2), pp. 294-301, 2014. [PDF]
S. Mehrkanoon, S. Mehrkanoon, J. A. K. Suykens, “Parameter estimation of delay differential equations: An integration-free LS-SVM approach,” Communications in Nonlinear Science and Numerical Simulation, Vol. 19, pp. 830-841, 2014. [PDF]
X. Huang, S. Mehrkanoon, J. A. K. Suykens, “Support vector machines with piecewise linear feature mapping,” Neurocomputing, Vol. 117, pp. 118-127, 2013. [PDF]
S. Mehrkanoon, J. A. K. Suykens, “LS-SVM approximate solution to linear time varying descriptor systems,” Automatica, 48(10), pp. 2502-2511, 2012. [PDF]
S. Mehrkanoon, T. Falck, J. A. K. Suykens, “Approximate solutions to ordinary differential equations using least squares support vector machines,” IEEE Trans. Neural Netw. Learning Syst, 23(9), pp. 1356-1367, 2012. [PDF]
S. Mehrkanoon, Z. A. Majid, M. Suleiman, K. I. Othman, Z. B. Ibrahim, “3-Point implicit block multistep method for the solution of first order ODEs,” Bull. Malays. Math. Sci. Soc, 35(2A), pp. 547-555, 2012. [PDF]
S. Mehrkanoon, “A direct variable step block multistep method for solving general third-order ODEs,” Numerical Algorithms, 57(1), pp. 53-66, 2011. [PDF]
S. Mehrkanoon, Z. A. Majid, M. Suleiman, “A variable step implicit block multistep method for solving first-order ODEs,” Journal of Computational and Applied Mathematics, 233(9), pp. 2387-2394, 2010. [PDF]
Z. A. Majid, S. Mehrkanoon, K. I. Othman, M. Suleiman, “Implementation of MPI environment for solving large systems of ODEs using block method,” WSEAS Transactions on Mathematics, 9(10), pp. 801-810, 2010. [PDF]
S. Mehrkanoon, Z. A. Majid, M. Suleiman, “Implementation of 2-point 2-step methods for the solution of first order ODEs,” Appl. Math. Sci, 4(7), pp. 305-316, 2010. [PDF]
S. Mehrkanoon, M. Suleiman, Z. A. Majid, K. I. Othman, “Parallel solution in space of large ODEs using block multistep method with step size controller,” European Journal of Scientific Research, 36(3), pp. 491-501, 2009. [PDF]
S. Mehrkanoon, M. Suleiman, Z. A. Majid, “Block method for numerical solution of fuzzy differential equations,” Int. Math. Forum, 4(46), pp. 2269-2280, 2009. [PDF]
S. Mehrkanoon, M. Suleiman, Z. A. Majid, “Spline approach to the solution of Sturm-Liouville problem,” Int. J. Contemp. Math. Sciences, 4(12), pp. 577-586, 2009.
S. Mehrkanoon, X. Huang, J.A.K. Suykens, “Learning from Partially Labeled Data”, accepted for publication in Proc. of the 28th European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, 2020.
Kiki van der Heijden, S. Mehrkanoon, “Modelling human sound localization with deep neural networks”, accepted for publication in the Proc. of the 28th European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, 2020.
S. Mehrkanoon, M. B. Blaschko, J.A.K. Suykens, “Shallow and Deep Models for Domain Adaptation problems,” Proc. of the 26th European Symposium on Artificial Neural Networks (ESANN), Apr. 2018, Bruges, Belgium, pp. 291-299.
S. Mehrkanoon, A. Zell, J.A.K. Suykens, “Scalable Hybrid Deep Neural Kernel Networks,” Proc. of the 25th European Symposium on Artificial Neural Networks (ESANN), Apr. 2017, Bruges, Belgium, pp. 17-22.
S. Mehrkanoon, J.A.K. Suykens, “Scalable Semi-Supervised Kernel Spectral Learning using Random Fourier Features,” in Proc. of the IEEE Symposium Series on Computational Intelligence (SSCI-CIDM), Dec. 2016, Athens, Greece, pp.1-8 [PDF].
S. Mehrkanoon, O. M. Agudelo, J.A.K. Suykens, “Incremental multi-class semisupervised clustering regularized by Kalman filtering,” In proc. of the 28th Benelux Conference on Artificial Intelligence, BNAIC 2016, November 10-11, 2016, Amsterdam, Netherlands. [PDF]
Yuri A.W. Shardt, Kai Zhang, S.X. Ding, S. Mehrkanoon, J.A.K. Suykens, X. Yang, and K. Peng (2016). “Control with Soft Sensors and Multiple Operating Points,” In proc. of the 66th Canadian Chemical Engineering Conference, 16th-19th October, 2016, Quebec, Canada. [PDF]
S. Mehrkanoon, J.A.K. Suykens, “Multi-label semi-supervised learning using regularized Kernel spectral clustering,” In proc. of IEEE World Congress on Computational Intelligence (IEEE WCCI/IJCNN 2016), July 24th-29th, 2016, Vancouver, Canada. [PDF]
Z. Karevan, S. Mehrkanoon, J. A. K. Suykens, “Black-box modeling for temperature prediction in weather forecasting,” In Proc. of the International Joint Conference on Neural Networks (IJCNN), Jul. 2015, Killarney, Ireland. [PDF]
S. Mehrkanoon, O. M. Agudelo, R. Mall, J. A. K. Suykens, “Hierarchical Semi-Supervised Clustering using KSC based model,” In Proc. of the International Joint Conference on Neural Networks 2015(IJCNN), Jul. 2015, Killarney, Ireland. [PDF]
S. Mehrkanoon, J. A. K. Suykens, “Large scale semi-supervised learning using KSC based model,” In proc. of the IEEE World Congress on Computational Intelligence (IEEE WCCI/IJCNN), Jul. 2014, Beijing, China, pp. 4152-4159. [PDF]
R. Mall, S. Mehrkanoon, R. Langone, J. A. K. Suykens, “Optimal Reduced Sets for Sparse Kernel Spectral Clustering,” In proc. of the IEEE World Congress on Computational Intelligence (IEEE WCCI/IJCNN), Jul. 2014, Beijing, China, pp. 2436 - 2443. [PDF]
R. Castro, S. Mehrkanoon, A. Marconato, J. Schoukens, J. A. K. Suykens, “SVD truncation schemes for fixed-size kernel models,” In proc. of IEEE World Congress on Computational Intelligence (IEEE WCCI/IJCNN), July. 2014, Beijing, China, pp. 3922-3929. [PDF]
S. Mehrkanoon, R. Quirynen, M. Diehl, J. A. K. Suykens, “LSSVM based initialization approach for parameter estimation of dynamical systems,” In proc. of International Conference on Mathematical Modeling in Physical Sciences (ICMSQUARE), Sep. 2013, Pragues, Czech Republic. [PDF]
S. Mehrkanoon, J. A. K. Suykens, “Non-parallel semi-supervised classification based on kernel spectral clustering,” In proc. of International Joint Conference on Neural Networks (IJCNN), Aug. 2013, Dallas, USA, pp. 2311-2318. [PDF]
S. Mehrkanoon, J. A. K. Suykens, “LS-SVM based solution for delay differential equations,” International Conference on Mathematical Modeling in Physical Sciences (ICM-SQUARE), Sep. 2012, Budapest, Hungary. [PDF]
S. Mehrkanoon, T. Falck, J. A. K. Suykens, “Parameter estimation for time varying dynamical systems using least squares support vector machines,” In proc. of the 16th IFAC Symposium on System Identification (SYSID), Jul. 2012, Brussels, Belgium, pp. 1300-1305. [PDF]
S. Mehrkanoon, L. Jiang, C. Alzate, J. A. K. Suykens, “Symbolic computing of LS-SVM based models,” 19th European Symposium on Artificial Neural Networks (ESANN), Apr. 2011, Bruges, Belgium, pp. 183-188. [PDF]
Z. A. Majid, S. Mehrkanoon, K. I. Othman, “Parallel block method for solving large systems of ODEs using MPI,” 4th International Conference on Applied Mathematics, Simulation, Modelling (ASM), Corfu Island, Greece, 2010. [PDF]
S. Mehrkanoon, Z. A. Majid, M. Suleiman, K. I. Othman, Z. B. Ibrahim, “3-Point implicit block multistep method half gauss seidel for the solution of first order ODEs,” In proc. of 5th Asian Mathematical Conference (AMC), Kuala Lumpur, Malaysia, 2009. [PDF]
"LS-SVM based solutions to differential equations", May 2019, invited talk at SMAI2019, France.
``Data Science Research Seminar", Dec.2018, Invited talk at Institute of Data Science, Maastricht University.
``Incorporation of prior knowledge into Kernel based models", Jun.2016, Invited talk at Institute of Control and Complex Systems (AKS), University of Duisburg-Essen, Germany.
"Teaching machine to learn the solution of dynamical system'', Mar.2016, Invited Talk at CPAMI, University of Waterloo. [Slides]
``Online semi-supervised clustering regularized by Kalman filtering", 34th Benelux Meeting on Systems and Control, Lommel, Belgium, March 24-26, 2015.
``Online video segmentation using semi-supervised learning", European Research Network on System Identification (ERNSI’2014), Sep.2014, Oostende, Belgium.
``Semi-supervised learning using the KSC core model", A-DATADRIVE-B Progress Meeting, Mar.2014, KU Leuven, Belgium.
``LS-SVM approximate solutions to PDEs", 33rd Benelux Meeting on Systems and Control, Heijen/Nijmegen, Netherlands, March 25-27, 2014. [Book of Abstracts]
``LS-SVM based solution for PDEs", IAP DYSCO Study Day: Dynamical Systems, Control and Optimization, Nov.2013, Brussels, Belgium.
"Non-parallel semi-supervised classification", International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines: Theory and Applications (ROKS), Jul.2013, KU Leuven, Belgium. [Poster]
``Parameter estimation of delay differential equations", IAP DYSCO Study Day: Dynamical Systems, Control and Optimization, May.2013, UMons, Mons, Belgium.
``LS-SVM approximate solution to DAE systems", IAP DYSCO Study Day: Dynamical Systems, Control and Optimization, Oct.2012, Louvain-la-Neuve, Belgium.
``An Oral Presentation. Seminars on Optimization in Engineering (WG2), Sep.2012, KU Leuven, Belgium.
``A Poster Presentation. OPTEC Workshop on Moving Horizon Estimation and System Identification, Aug.2012 , KU Leuven, Belgium.
``LS-SVM approach for solving linear descriptor systems", 31st Benelux Meeting on Systems and Control, Heijen/Nijmegen, Netherlands, March 27-29, 2012. [Book of Abstracts]
``Parallel Block solver for solving large scale IVPs", Research Seminar at University Putra Malaysia, Apr.2010, Selangor, Malaysia.
``A variable Block method for solving ODEs", Research Seminar at University Putra Malaysia, May.2009, Selangor, Malaysia.
``Approximate solution of SLP’s eigenvalues by using Quintic spline function", Research Seminar at University Putra Malaysia, Jul.2008, Selangor, Malaysia.
``Chebyshev polynomial solution of linear-differential equation", Research Seminar at University Putra Malaysia, Dec.2007. Selangor, Malaysia.
Siamak Mehrkanoon, "Incorporation of Prior Knowledge into Kernel Based Models," KU Leuven, July.2015
Siamak Mehrkanoon, "Solving Ordinary Differential Equations Using Block Multistep Method," , UPM , March.2011.
Invited talk on "Deep Short-Term Weather Elements Forecasting" at Deep Learning and Artificial Intelligence Summer/Winter School 2023 (DLAI7), July 2023.
Introduction to Self-Supervised Learning and its Applications, Tutorial at IEEE-WCCI-IJCNN, July, 2022.
Theory and Applications of Partially Labeled Data Learning, Special Session at IJCNN, 2020, July , Glasgow (UK).
Invited Tutorial at https://cml.rhul.ac.uk/copa2020/ (Youtube)
Deep Learning: Artificial Neural Networks and Kernel based Models, Tutorial at IJCNN 2019, Budapest.
LS-SVM based solutions to differential equations, May 2019, smai2019, Lorient, France.
Deep Learning, 12th Maastricht Summer School on Data Science, June 26-28, 2019.
`Data Science Research Seminar, Dec.2018, Institute of Data Science, Maastricht University.
Kernel based models and applications, May 2018, Shanghai Jiao Tong University, Shanghai, China.
Incorporation of prior knowledge into Kernel based models", Jun.2016, Institute of Control and Complex Systems (AKS), Duisburg, Germany.
Semi-Supervised Learning, October 2016, Tubingen University, Germany.
Teaching machine to learn the solution of dynamical system, Mar.2016, CPAMI, University of Waterloo, Canada. [Slides]