Publications

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Journal papers (refereed):

  1. M.N. Fekri*, K. Grolinger, S. Mir, Asynchronous Adaptive Federated Learning for Distributed Load
    Forecasting with Smart Meter Data    , International Journal of Electrical Power and Energy Systems, 2023, accepted.
  2. R. Skala*, M.A.T.A. Elgalhud*, K. Grolinger, S. Mir, Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging, Energies, Vol. 16, No. 10, 2023.
  3. D. Gholamiangonabadi*, K. Grolinger, Personalized models for human activity recognition with wearable sensors: Deep neural networks and signal processing, Applied Intelligence, 2023.
  4. K. Dunphy, M.N. Fekri*, K. Grolinger, A. Sadhu, Data Augmentation for Deep Learning-based Multiclass Structural Damage Detection using Limited Information, Sensors, Vol. 22, No. 16, 2022.
  5. F. AlMahamid*, K. Grolinger, Autonomous Unmanned Aerial Vehicle Navigation using reinforcement Learning: A Systematic Review, Engineering Applications of Artificial Intelligence, 2022.
  6. L’Heureux*, K. Grolinger, M.A.M. Capretz, Transformer-Based Model for Electrical Load Forecasting, Energies, 2022.
  7. K. Rai*, F. Hojatpanah, F. Badrkhani Ajaei, J.M. Guerrero, K. Grolinger, Deep Learning for High-Impedance Fault Detection and Classification: Transformer-CNN, Neural Computing and Applications, 2022.
  8. M.N. Fekri*, K. Grolinger, Syed Mir, Distributed Load Forecasting using Smart Meter Data: Federated Learning with Recurrent Neural Networks, International Journal of Electrical Power and Energy Systems, Vol. 137, 2022.
  9. R.K. Jagait*, M.N. Fekri*, K. Grolinger, Syed Mir, Load Forecasting Under Concept Drift: Online Ensemble Learning with Recurrent Neural Network and ARIMA, IEEE Access, 2021.
  10. K. Rai*, F. Hojatpanah, F. Badrkhani Ajaei, K. Grolinger, Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders, Energies, Vol. 14, No. 12, 2021.
  11. M.N. Fekri, H. Patel, K.Grolinger, V. Sharma, Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network, Applied Energy, Vol. 282, 2021.
  12. A.M. Ghosh*, K. Grolinger, Edge-Cloud Computing for IoT Data Analytics: Embedding Intelligence in the Edge with Deep Learning, IEEE Transactions on Industrial Informatics, 2020.
  13. D. Gholamiangonabadi*, N. Kiselov*, K. Grolinger, Deep Neural Networks for Human Activity Recognition with Wearable Sensors: Leave-one-subject-out Cross-validation for Model Selection, IEEE Access, 2020.
  14. L. Sehovac*, K. Grolinger, Deep Learning for Load Forecasting: Sequence to Sequence Recurrent Neural Networks with Attention, IEEE Access, doi:10.1109/ACCESS.2020.2975738, 2020.
  15. M.N. Fekri*, A.M. Ghosh*, K. Grolinger, Generating Energy Data for Machine Learning with Recurrent Generative Adversarial Networks, Energies, Vol.13, No. 1, 2019.
  16. Y. Tian*, L. Sehovac*, K. Grolinger, Similarity-Based Chained Transfer Learning for Energy Forecasting with Big Data, IEEE Access, doi:10.1109/ACCESS.2019.2943752, 2019. 
  17. M.M. ElGayyar, H.F. ElYamany, K. Grolinger, M.A.M. Capretz, Blockchain-Based Federated Identity and Auditing, International Journal of Blockchains and Cryptocurrencies, Accepted, 2019.
  18. D. Bachmann, K. Grolinger, H. ElYamany, W.Higashino, M.A.M. Capretz, M. Fekri, B. Gopalakrishnan, (CF)2 Architecture: Contextual Collaborative Filtering, Information Retrieval Journal, Springer, pp. 1-24, doi: 10.1007/s10791-018-9332-3, 2018.
  19. M. Ribeiro, K. Grolinger, H.F. ElYamany, W.A. Higashino, M.A.M. Capretz, Transfer Learning with Seasonal and Trend Adjustment for Cross-Building Energy Forecasting, Energy and Buildings, Elsevier, Vol. 165, pp. 352-363, doi: 10.1016/j.enbuild.2018.01, 2018.
  20. K. Grolinger, H.F. ElYamany, W.A. Higashino, M.A.M. Capretz, L. Seewald, Energy Slices: Benchmarking with Time Slicing, Energy Efficiency, Springer, Vol. 11, No. 2, pp.  pp 521–538, doi: 10.1007/s12053-017-9582-8, 2018.
  21. A. L’Heureux, K. Grolinger, H.F. ElYamany, M.M.A. Capretz, Machine Learning with Big Data: Challenges and Approaches, IEEE Access, Vol. 5, pp. 7776 - 7797, doi: 10.1109/ACCESS.2017.2696365, 2017.
  22. D.B. Araya, K. Grolinger, H.F. ElYamany, M.A.M. Capretz, G.T. Bitsuamlak, An Ensemble Learning Framework for Anomaly Detection in Building Energy Consumption, Energy and Buildings, Elsevier, Vol. 144, pp. 191–206, doi:10.1016/j.enbuild.2017.02.058, 2017.
  23. K. Grolinger, A. L’Heureux, M.A.M. Capretz, L. Seewald, Energy Forecasting for Event Venues: Big Data and Prediction Accuracy, Energy and Buildings, Elsevier, Vol. 112, pp. 222–233,  doi:10.1016/j.enbuild.2015.12.010, 2016.
  24. K. Grolinger, E. Mezghani, M.A.M. Capretz, E.Exposito, Collaborative Knowledge as a Service Applied to the Disaster Management Domain, International Journal of Cloud Computing, Inderscience, Vol. 4, No. 1, pp. 5-27, doi:10.1504/IJCC.2015.067706 2015
  25. K. Grolinger, W.A. Higashino, A. Tiwari, M.A.M. Capretz, Data Management in Cloud Environments: NoSQL and NewSQL Data Stores, Journal of Cloud Computing: Advances,Systemsand Application, Springer, Vol. 2, No. 22, doi:10.1186/2192-113X-2-22, 2013.
  26. K. Grolinger, M.A.M. Capretz, A. Cunha, S. Tazi, Integration of Business Process Modeling and Web Services: A Survey, Service OrientedComputingand Applications, Springer, Vol. 8, No. 2, pp. 105–128, doi:10.1007/s11761-013-0138-2, 2013.
  27. B.Muslimi, K. Grolinger, M.A.M. Capretz, M. Benko, EEF-CAS: An Effort Estimation Framework with Customizable Attribute Selection, International Journal of Advancements in Computing Technology, Vol., 5. No. 13, 2013.
  28. K. Grolinger, M.A.M Capretz, A Unit Test Approach for Database Schema Evolution, Information and Software Technology, Elsevier, Vol. 53, No. 2, pp. 159-170, doi:10.1016/j.infsof.2010.10.002, 2011.
  29. B. Jerbic, K. Grolinger, B. Vranjes, Autonomous Agent Based on Reinforcement Learning and Adaptive Shadowed Network. Artificial Intelligence in Engineering. Vol. 13, No. 2, pp. 141-157, 1999.
  30. B. Jerbic, K. Grolinger, B. Vranjes, Autonomous Robotic Task Reasoning in Unpredictable Assembly Conditions, Automatika, Vol. 37 (1-2), Zagreb, Croatia, pp. 37-45. 1996.

Conference papers (refereed):

  1. M. Buwaneswaran*, T. Fonseka*, A. Narayan, and K. Grolinger, Improving Adversarial Robustness of Few-shot Learning with Contrastive Learning and Hypersphere Embedding, Proc. of the IEEE International Conference on Machine Learning and Applications, 2023.
  2. Z. Han*, K. Grolinger, M. Capretz, S. Mir, Scheduling Electric Vehicle Charging for Grid Load Balancing, Proc. of the 49th Annual Conference of the IEEE Industrial Electronics Society, 2023.
  3. F. AlMahamid*, K.Grolinger, Agglomerative Hierarchical Clustering with Dynamic Time Warping for Household Load Curve Clustering, Proc. of the IEEE Canadian Conference on Electrical and Computer Engineering, 2022, Accepted.
  4. F. AlMahamid*, H. Lutfiyya, K.Grolinger, Virtual Sensor Middleware: Managing IoT Data for the Fog-Cloud Platform, Proc. of the IEEE Canadian Conference on Electrical and Computer Engineering, 2022, Accepted.
  5. S. Vecile, K. Lacroix*, K. Grolinger, J. Samarabandu, Malicious and Benign URL Dataset Generation Using Character-Level LSTM Models, Proc. of the IEEE Conference on Dependable and Secure Computing, Edinburgh, England, 2022.
  6. S.A. Salinas*, M. Elgalhud*, L. Tambakis, S. Salunke, K. Patel, H. Ghenniwa, A. Ouda, K. McIsaac, K. Grolinger, A.L. Trejos, Comparison of Machine Learning Techniques for Activities of Daily Living Classification with Electromyographic Data, Proc. of the IEEE International Conference on Rehabilitation Robotics, Rotterdam, Netherlands, 2022.
  7. F. AlMahamid, K.Grolinger, Reinforcement Learning Algorithms: An Overview and Classification, Proc. of the IEEE Canadian Conference on Electrical and Computer Engineering, 2021. Accepted.
  8. Y. Yang, N. Qamar, P. Liu, K. Grolinger, W. Wang, Z. Li, Z. Liao, ServeNet: A Deep Neural Network for Web Services Classification, Proc. of the IEEE International Conference on Web Services, 2020.
  9. L. Sehovac, C. Nesen, K. Grolinger, Forecasting Building Energy Consumption with Deep Learning: A Sequence to Sequence Approach, Proc. of the IEEE International Congress on Internet of Things, Milano, Italy, 2019.
  10. A. M. Ghosh, K. Grolinger, Deep Learning: Edge-Cloud Data Analytics for IoT, Proc. of the IEEE Canadian Conference on Electrical and Computer Engineering, Edmonton, Canada, 2019.
  11. X. M. Zhang, K. Grolinger, M. A. M. Capretz, Forecasting Residential Energy Consumption: Single Household Perspective, Proc. of the IEEE International Conference on Machine Learning and Applications, 2018.
  12. A. L’Heureux, K. Grolinger, W.A. Higashino, M. A. M. Capretz, A Gamification Framework for Sensor Data Analytics, Proc. of the IEEE International Congress on Internet of Thing, Honolulu, Hawaii, USA, 2017.
  13. N. L. Tasfi, W.A. Higashino, K. Grolinger, M. A. M. Capretz, Deep Neural Networks With Confidence Sampling for Electrical Anomaly Detection, Proc. of the IEEE International Conference on Smart Data, Exeter, England, 2017, accepted.
  14. K. Grolinger, M.A.M. Capretz, L. Seewald, Energy Consumption Prediction with Big Data: Balancing Prediction Accuracy and Computational Resources, Proc. of the IEEE BigData Congress, San Francisco, California, 27 Jun-2 Jul, 2016.
  15. D.B. Araya, K. Grolinger, H.F. El Yamany, M.A.M. Capretz, G.T. Bitsuamlak, Collective Contextual Anomaly Detection Framework for Smart Buildings , Proc. of the IEEE World Congress in Computational Intellegence, Vancouver, Canada, 24-29 Jul, 2016.
  16. M. Ribeiro, K. Grolinger, M.A.M. Capretz, MLaaS: Machine Learning as a Service , Proc. of the IEEE International Conference on Machine Learning and Applications, Miami, Florida, USA, 9-11 Dec, 2015
  17. S.S. Abdelkader, K. Grolinger, M.A.M. Capretz, Predicting energy demand peak using M5 model trees, Proc. of the IEEE International Conference on Machine Learning and Applications, Miami, Florida, USA, 9-11 Dec, 2015.
  18. A. Zagar, K. Grolinger, M.A.M Capretz, L. Seewald, Energy Cost Forecasting for Event Venues, Proc. of the IEEE Electrical Power & Energy Conference, London, Ontario, Canada, 26-28 Oct, 2015.
  19. H. F. ElYamany, M. F. Mohamed, K. Grolinger, M. M. A. Capretz, A Generalized Service Replication Process in Distributed Environments, Proc. of the 5th International Conference on Cloud Computing and Services Science, Lisbon, Portugal, 20-22 May, 2015.
  20. K. Grolinger, M. Hayes, W. Higashino, A. L'Heureux, D. S. Allison, M. A. M. Capretz, Challenges for MapReduce in Big Data, Proc. of the IEEE 10th 2014 World Congress on Services (SERVICES 2014), Alaska, USA, June 27-July 2, 1014
  21. K. Grolinger, E. Mezghani, M.A.M. Capretz, E. Exposito, Knowledge as a Service Framework for Disaster Data Management, The 22nd IEEE WETICE conference, Hammamet, Tunisia, pp. 313-318, 2013.
  22. K. Grolinger, M.A.M. Capretz, J.R. Marti, K.D. Srivastava, Ontology–based Representation of Simulation Models, The 24th International Conference on Software Engineering and Knowledge Engineering, San Francisco Bay, California, USA, pp. 432-437, 2012.
  23. K. Grolinger, M.A.M. Capretz, Autonomic Database Management: State of the Art and Future Trends, The 27th International Conference on Computers and Their Applications (CATA), Las Vegas, Nevada, USA, pp.276-281, 2012.
  24. K. Grolinger, K.P. Brown, M.A.M. Capretz, From Glossaries to Ontologies: Disaster Management Domain, The 23rd International Conference on Software Engineering and Knowledge Engineering, Miami Beach, Florida, USA, pp. 402-407, 2011.
  25. K. Grolinger, A. Shypanski, G.S. Gill, M.A.M. Capretz, Federated Critical Infrastructure Simulators: Towards Ontologies for Support Of Collaboration, The 24th IEEE Canadian Conference on Electrical and Computer Engineering, Niagara Falls, Ontario, Canada, pp. 1503-1506, 2011.
  26. K.P. Brown, K. Grolinger, M.A.M. Capretz, Data Providing Web Service-Based Integration Framework for use in a Health Care Context, The 24th IEEE Canadian Conference on Electrical and Computer Engineering 2011, Niagara Falls, Ontario, Canada, pp. 1069–1072, 2011.
  27. K. Grolinger, B Jerbic, B. Vranjes, Autonomous Robot Behavior Based on Neural Networks, Applications and Science of Artificial Neural Networks III, Orlando, Florida, USA, SPIE Press, pp. 2038-2046, 1997.
  28. B. Jerbic, K Grolinger, B. Vranjes, Simulation of Intelligent Robot Behavior Based on Reinforcement Learning and Neural Network Approach, The 11th International Conference on Artificial Intelligence in Engineering, Southampton, NY, USA, pp. 450-465, 1996.
  29. B. Jerbic, K. Grolinger, B. Vranjes, Simulation of Robotic Learning in Assembly Process, The 7th International DAAAM Symposium, Vienna, Austria, pp. 185-187, 1996
  30. B. Vranjes, K. Grolinger, B. Jerbic, Modified Fuzzy ART Neural Network in Group Technologies, The 7th International DAAAM Symposium, Vienna, Austria, pp. 185-187, 1996.
  31. B. Jerbic, K. Grolinger, B. Vranjes, Autonomous Robotic Task Reasoning in Unpredictable Assembly Conditions, The 13th Conference BIAM 96, Zagreb, Croatia, pp. B1-B6, 1996.
  32. B. Vranjes, K. Grolinger, B. Jerbic, Cellular Manufacturing Formation with Modified Fuzzy ART Neural Network, The 13th Conference BIAM 96, Zagreb, Croatia, pp. J5-J8, 1996.

Book chapters:

  1. K. Grolinger, E. Mezghani, M.A.M. Capretz, E. Exposito, Knowledge as a Service Framework for Collaborative Data Management in Cloud Environments - Disaster Domain, in Book: Managing Big Data in Cloud Computing Environments, IGI Global, in press.