Kaj-Mikael Björk, PhD

Executive Director

Linkedin Envelope

Biography

Kaj-Mikael is the Head of Department of Business Management and Analytics at Arcada. Prior to that, Kaj-Mikael was working as a Senior Lecturer in Logistics (Arcada) and Assistant Professor in Information Systems (Åbo Akademi). The borderline between economics and IT has long fascinated him, as well as inspired him to pursue achievements in both education and research. He has held approx. 15 different courses in the fields of Logistics and Management Science and Engineering. Within the research projects he has participated in, he has completed approximately 60 scientific peer reviewed articles with an H-index of 10 (Google scholar). As Head of Department, Kaj-Mikael also participates actively in many administrative tasks and he is a member of the university’s steering group. His research interests are in information systems, analytics, supply chain management, machine learning, fuzzy logic, and optimization.

Selected publications

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Akusok, A. et al. Adding reliability to ELM forecasts by confidence intervals. Neurocomputing 232–241 (2017). http://doi.org/10.1016/j.neucom.2016.09.021
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Sovilj, D. et al. Extreme Learning Machine for Missing Data using Multiple Imputations. Neurocomputing 174, Part A, 220–231 (2016).
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Eirola, E., Akusok, A., Björk, K.-M., Johnson, H. & Lendasse, A. Predicting Huntington’s Disease: Extreme Learning Machine with Missing Values. in Proceedings of the International Conference on Extreme Learning Machines (ELM 2016) (2016).
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Akusok, A. et al. ELMVIS+: Improved Nonlinear Visualization Technique Using Cosine Distance and Extreme Learning Machines. Proceedings of ELM-2015 2, 357–369 (2016).
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Björk, K.-M. & Lundell, A. Global optimization of a portfolio adjustment problem under credibility measures. International Journal of Operational Research 25(4) (2016). http://doi.org/10.1504/IJOR.2016.075292
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Björk, K.-M. & Mezei, J. A heuristical solution method to separable nonlinear programming problems. International Journal of Mathematics in Operational Research 9(2) (2016). http://doi.org/10.1504/IJMOR.2016.078002
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Eirola, E. et al. Extreme Learning Machines for Multiclass Classification: Refining Predictions with Gaussian Mixture Models. in Advances in Computational Intelligence 9095, 153–164 (LNCS, 2015).
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Akusok, A. et al. Arbitrary Category Classification of Websites Based on Image Content. IEEE Computational Intelligence Magazine 10, 30–41 (2015).
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Akusok, A., Björk, K.-M., Miche, Y. & Lendasse, A. High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications. IEEE Access 1011–1025 (2015). http://doi.org/10.1109/ACCESS.2015.2450498
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Swaney, C., Akusok, A., Björk, K.-M., Miche, Y. & Lendasse, A. Efficient Skin Segmentation via Neural Networks: HP-ELM and BD-SOM. in (2015). http://doi.org/10.1016/j.procs.2015.07.317
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Akusok, A. et al. MD-ELM: Originally Mislabeled Samples Detection using OP-ELM Model. Neurocomputing 159, 242–250 (2015).
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Björk, K.-M. & Mezei, J. An Economic Production Quantity Problem with Backorders and fuzzy cycle times. International journal of fuzzy systems 28(4), 1861--1868 (2015).
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Björk, K.-M. & Mezei, J. A fuzzy MILP-model for the optimization of vehicle routing problem. Journal of Intelligent and Fuzzy System 26, 1349--1361 (2014).
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Björk, K.-M. A Multi-item Fuzzy Economic Production Quantity Problem with a Finite Production Rate. International Journal of Production Economics 135, 702--707 (2012).