Hadi Banaee

Hadi Banaee


AASS Research Center
School of Science and Technology
Örebro University
70182 Örebro, Sweden
Room T2233
Phone +46 (0)19 30 36 58

Google Scholar

Since September 2012, I have been working towards the Ph.D degree at Örebro University, center for applied autonomous sensor systems (AASS), Örebro, Sweden. I am currently engaged in the research on the fields of data mining and health informatics. In particular, my interests include wearable sensor data mining, symbolic representation of time series, and natural language generation of numeric data. My current activity is focused on rule mining in physiological sensor data and finding a semantic model for the descriptive rules in order to exploit a symbolic representation of physiological data streams.

I received my Master degree in Computer Science from Tehran Polytechnic , Iran in 2011 in the field of Computational Geometry with studying the utility of geometrical algorithms in Bioinformatics problems, particularly gene expression clustering.


[1] H. Banaee, G. Chimamiwa, M. Alirezaie and A. Loutfi. Explaining Habits and Changes of Activities in Smart Homes. 2020BibTeX | DiVA | PDF ]
[2] G. Chimamiwa, M. Alirezaie, H. Banaee, U. Köckemann and A. Loutfi. Towards Habit Recognition in Smart Homes for People with Dementia. In Ambient Intelligence : 15th European Conference, AmI 2019, Rome, Italy, November 13–15, 2019, Proceedings, 11912(11912):363-369, 2019BibTeX | DiVA ]
[3] H. Banaee, E. Schaffernicht and A. Loutfi. Data-Driven Conceptual Spaces : Creating Semantic Representations for Linguistic Descriptions of Numerical Data. The journal of artificial intelligence research, 63:691-742, 2018BibTeX | DiVA | PDF ]
[4] H. Banaee. From Numerical Sensor Data to Semantic Representations : A Data-driven Approach for Generating Linguistic Descriptions. Örebro University, School of Science and Technology, Ph.D. Thesis, 2018BibTeX | DiVA | PDF ]
[5] A. Vajdi, N. Haspel and H. Banaee. A New DP Algorithm for Comparing Gene Expression Data Using Geometric Similarity. In Proceedings 2015 IEEE International Conference on Bioinformatics and Biomedicine, pages 1157-1161, 2015BibTeX | DiVA ]
[6] H. Banaee and A. Loutfi. Data-driven rule mining and representation of temporal patterns in physiological sensor data. IEEE journal of biomedical and health informatics, 19(5):1557-1566, 2015BibTeX | DiVA ]
[7] H. Banaee, M. U. Ahmed and A. Loutfi. Descriptive Modelling of Clinical Conditions with Data-driven Rule Mining in Physiological Data. In Proceedings of the 8th International conference of Health Informatics (HEALTHINF 2015) 2015BibTeX | DiVA | PDF ]
[8] M. U. Ahmed, H. Banaee, X. Rafael-Palou and A. Loutfi. Intelligent Healthcare Services to Support Health Monitoring of Elderly. In INTERNET OF THINGS : USER-CENTRIC IOT, PT I, 150(150):178-186, 2015BibTeX | DiVA | PDF ]
[9] H. Banaee and A. Loutfi. Using Conceptual Spaces to Model Domain Knowledge in Data-to-Text Systems. In Proceedings of the 8th International Natural Language Generation Conference, pages 11-15, 2014BibTeX | DiVA | PDF ]
[10] H. Banaee, M. U. Ahmed and A. Loutfi. A framework for automatic text generation of trends in physiological time series data. In IEEE International Conference on Systems, Man, and Cybernetics, 13-16 Oct. 2013, Manchester, pages 3876-3881, 2013BibTeX | DiVA ]
[11] H. Banaee, M. U. Ahmed and A. Loutfi. Data mining for wearable sensors in health monitoring systems : a review of recent trends and challenges. Sensors, 13(12):17472-17500, 2013BibTeX | DiVA | PDF ]
[12] M. U. Ahmed, H. Banaee and A. Loutfi. Health monitoring for elderly : an application using case-based reasoning and cluster analysis. ISRN Artificial Intelligence, 2013(2013):1-11, 2013BibTeX | DiVA | PDF ]
[13] H. Banaee, M. U. Ahmed and A. Loutfi. Towards NLG for Physiological Data Monitoring with Body Area Networks. In 14th European Workshop on Natural Language Generation, pages 193-197, 2013BibTeX | DiVA | PDF ]