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Hadi Banaee

Title: Postdoctoral Researcher School/office: School of Science and Technology

Email:

Phone: +46 19 303658

Room: T2233

Hadi Banaee

About Hadi Banaee

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.

Publications

Articles in journals |  Conference papers |  Doctoral theses, comprehensive summaries | 

Articles in journals

Banaee, H. & Loutfi, A. (2015). Data-driven rule mining and representation of temporal patterns in physiological sensor data. IEEE journal of biomedical and health informatics, 19 (5), 1557-1566.
Ahmed, M. U. , Banaee, H. & Loutfi, A. (2013). Health monitoring for elderly: an application using case-based reasoning and cluster analysis. ISRN Artificial Intelligence, 2013 (2013), 1-11.

Conference papers

Vajdi, A. , Haspel, N. & Banaee, H. (2015). A New DP Algorithm for Comparing Gene Expression Data Using Geometric Similarity. In: Proceedings 2015 IEEE International Conference on Bioinformatics and Biomedicine. Paper presented at IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2015), Washington, DC, USA, November 9-12, 2015 (pp. 1157-1161). New York: IEEE conference proceedings.
Banaee, H. , Ahmed, M. U. & Loutfi, A. (2015). 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). Paper presented at HEALTHINF 2015 : HEALTHINF 8th International Conference on Health Informatics, 12-15 january, Lisabon, Portugal. SciTePress.
Ahmed, M. U. , Banaee, H. , Rafael-Palou, X. & Loutfi, A. (2015). Intelligent Healthcare Services to Support Health Monitoring of Elderly. In: INTERNET OF THINGS USER-CENTRIC IOT, PT I. Paper presented at 1st International Conference on IoT Technologies for HealthCare, HealthyIoT, October 27-29, Rome, Italy, 2014 (pp. 178-186). Springer.
Banaee, H. & Loutfi, A. (2014). Using Conceptual Spaces to Model Domain Knowledge in Data-to-Text Systems. In: Proceedings of the 8th International Natural Language Generation Conference. Paper presented at 8th International Natural Language Generation(INLG)Conference, 19-21 June, Philadelphia, Pennsylvania, USA (pp. 11-15). Association for Computational Linguistics.
Banaee, H. , Ahmed, M. U. & Loutfi, A. (2013). 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. Paper presented at IEEE International Conference on Systems, Man, and Cybernetics, 13-16 Oct. 2013, Manchester (pp. 3876-3881). IEEE conference proceedings.
Banaee, H. , Ahmed, M. U. & Loutfi, A. (2013). Towards NLG for Physiological Data Monitoring with Body Area Networks. In: 14th European Workshop on Natural Language Generation. Paper presented at 14th European Workshop on Natural Language Generation, Sofia, Bulgaria, August 8-9, 2013 (pp. 193-197).

Doctoral theses, comprehensive summaries

Banaee, H. (2018). From Numerical Sensor Data to Semantic Representations: A Data-driven Approach for Generating Linguistic Descriptions. (Doctoral dissertation). (Comprehensive summary) Örebro: Örebro University.