Bijan Raahemi
- Location
- DMS 6143
- Telephone
- 613-562-5800 x 4859
This email address is being protected from spambots. You need JavaScript enabled to view it. - Website
- telfer.uottawa.ca/raahemi/
Biography
Professor Raahemi received his Ph.D. in Electrical and Computer Engineering from the University of Waterloo, Canada, in 1997. Prior to joining the Telfer School of Management as an Associate Professor in Management Information Systems and Technology, Dr. Raahemi held several research positions in the telecommunications industry, including Nortel and Alcatel, focusing on computer networks architectures and services, dynamics of internet traffic, systems modelling, and performance analysis.
Professor Raahemi's work has appeared in peer-reviewed journals and conferences papers. He also holds several patents in data communications. His current research interests include data mining and knowledge discovery, information systems, business intelligence, data communications networks and services, service oriented architecture, systems modelling, and performance analysis. Dr. Raahemi established the Knowledge Discovery and Data mining (KDD) Research Laboratory compromising graduate students and researchers from multidisciplinary areas of E-Business Technology, Systems Science, and Engineering. The research projects in the KDD lab focus on two main streams: (a) novel techniques of data mining and machine learning with their emerging applications in business, healthcare, and engineering; and (b) Information systems, data communications, and service oriented organizations.
Professor Raahemi is a registered member of Professional Engineers of Ontario (PEO), a senior member of the Institute of Electrical and Electronics Engineering (IEEE), and a member of the Association for Computing Machinery (ACM). He is also a member of Ottawa-Carleton Institute for Electrical and Computer Engineering (OCIECE), and Ottawa-Carleton Institute for Computer Science (OCICS).
Publications during the last 7 years
Papers in Refereed Journals
- Obidallah, W.J., Raahemi, B. and Rashideh, W. 2022. Multi-Layer Web Services Discovery Using Word Embedding and Clustering Techniques. Data, 7(5): 57.
- Ashtiani, M.N. and Raahemi, B. 2021. Intelligent Fraud Detection in Financial Statements using Machine Learning and Data Mining: A Systematic Literature Review. IEEE Access.
- Riahi, M., Akbari, A., Nasersharif, B. and Raahemi, B. 2021. A new density-based subspace selection method using mutual information for high dimensional outlier detection. Knowledge-Based Systems Journal, 216.
- Pishgoo, B., Akbari, A. and Raahemi, B. 2020. A Hybrid Distributed Batch-Stream Processing Approach for Anomaly Detection. Information Sciences, 543: 309-327.
- Obidallah, W.J., Ruhi, U. and Raahemi, B. 2020. Clustering and Association Rules for Web Service Discovery and Recommendation: A Systematic Literature Review. SN Computer Science, 1(27): 1-33.
- Muhammed, M.T. , Obidallah, W.J. and Raahemi, B. 2018. Applying Deep Learning Techniques for Big Data Analytics: A Systematic Literature Review. Archives of Information Science and Technology, 1(1): 20-41.
- Cheraghchi, F., Abualhaol, I., Falcon, R., Abielmona, R. and Raahemi, B. 2018. Modeling the Speed-based Vessel Schedule Recovery Problem using Evolutionary Multiobjective Optimization. Information Sciences.
- Bigdeli, E., Mohammadi, M., Raahemi, B. and Matwin, S. 2018. Incremental anomaly detection using two-layer cluster-based structure. Information Sciences, 429(2018): 315–331.
- Obidallah, W.J. and Raahemi, B. 2017. Managing Changes in Service Oriented Virtual Organizations: A Structural and Procedural Framework to Facilitate the Process of Change. Journal of Electronic Commerce in Organizations, 15(1): 59-83.
- Dutta, I., Dutta, S. and Raahemi, B. 2017. Detecting Financial Restatements using Data Mining Techniques. Expert Systems with Applications, 90(C): 374-393.
- Shangying, X. and Raahemi, B. 2016. A Semantic-Based Service Discovery Framework for Collaborative Environments. International Journal of Simulation Modelling, 15(1): 83-96.
- Mohammadi, M., Al-Azab, F., Raahemi, B., Richards, G.S., Jaworska, N., Smith, D.C., de la Salle, S., Blier, P. and Knott, V. 2015. Data Mining EEG Signals in Depression for their Diagnostic Value. BMC Medical Informatics and Decision Making, 15(1): 108.
- Mohammadi, M., Mehraban, S.A., Bigdeli, E., Raahemi, B. and Akbari, A. 2015. An enhanced noise resilient K-associated graph classifier. Expert Systems with Applications, 42(21): 8283-8289.
- Asgharian, H., Akbari, A. and Raahemi, B. 2015. Feature Engineering for Detection of Denial of Service Attacks in Session Initiation Protocol. Journal of Security and Communication Networks, 8(8): 1587-1601.
- Tekieh, M., Raahemi, B. and Izadshenas, S.A. 2015. Analyzing Healthcare Coverage using Data Mining Techniques. International Journal of Society Systems Science, 7(3): 198-221.
- Mohammadi, M., Raahemi, B. and Akbari, A. 2015. A Geometric Density-based Sample Reduction Method. Intelligent Data Analysis: An International Journal, 19(6): 1233-1257.
- Bigdeli, E., Mohammadi, M., Raahemi, B. and Matwin, S. 2015. A Fast and Noise Resilient Cluster-based Anomaly Detection. Journal of Pattern Analysis and Applications.
Books
- Akhond Zadeh Noughabi, E., Raahemi, B., Albadwi, M. and Far, B. Handbook of Research on Data Science for Effective Healthcare Practice and Administration. Pennsylvania, USA: IGI Global Publishers, 2017.
Chapters in Books
- Kamali, A., Richards, G., Raahemi, B. and Danesh, M. A Framework and Architecture for Performance Management in Virtual Organizations. In Shah J. M. and William Y.. Applying Business Intelligence Initiatives in Healthcare and Organizational Settings. USA: IGI Global, 2018.
- Nasiri, S. and Raahemi, B. Non-Parametric Statistical Analysis of Rare Events in Healthcare: Case of Histological Outcome of Kidney Transplantation. In Akhond Zadeh Noughabi, E.. Handbook of Research on Data Science for Effective Healthcare Practice and Administration. Pennsylvania, USA: IGI Global Publishers, 2017.
- Tekieh, M., Raahemi, B. and Benchimol, E. Leveraging Applications of Data Mining in Healthcare using Big Data Analytics: An Overview. In Akhond Zadeh Noughabi, E.. Handbook of Research on Data Science for Effective Healthcare Practice and Administration. Pennsylvania, USA: IGI Global Publishers, 2017.
Funded Research during the last 7 years
| From-To | Source | Title | * | ** | Role | Amount |
|---|---|---|---|---|---|---|
| 2020-2022 | OCE and Ciena | Smart Factory | R | O | PI | $ 150,000 |
| 2018-2019 | MITACS & Bell Canada | Network Traffic Classification for Cyber Threat and Malware Detection | U | C | PI | $ 50,000 |
| 2017-2021 | NSERC - Discovery Grants Program | Outlier Detection in High-Dimensional Big Data using Bio-Inspired Methods for Emerging Applications in Engineering, Healthcare, and Business | R | C | PI | $ 120,000 |
| 2017-2018 | OCE, NSERC, NRC | Estimating Bus Passengers' Origin Destination Travel Route using Data Analytics on Wi-Fi and Bluetooth Signals | R | U | PI | $ 65,000 |
| 2017-2018 | MITACS & Bell Canada | Network Traffic Classification for Cyber Threat and Malware Detection | R | C | PI | $ 50,000 |
| 2016-2019 | MITACS and ICES | Mining of population-based routinely collected health data to determine risk factors associated with pediatric morbidity in Ontario. | R | O | PI | $ 90,000 |
| 2015-2016 | Ontario Ministry of Energy, and NSERC | SecCharge: Secure Electric Vehicle Ecosystem for Smart Grid | C | C | Co-I | $ 3,090,800 |
| 2015 | NSERC Engage | Capturing and Analyzing Data from SensorSuite's Devices using Big Data Analytics Techniques | R | C | PI | $ 25,000 |
| 2012-2017 | NSERC - Discovery Grants Program | Feature Engineering using Bio-Inspired Methods for the Internet Data Analytics | R | C | PI | $ 105,000 |
LEGEND:
*Purpose
C: Contract (R and D) | E: Equipment Grant | R: Research Grant | S: Support Award | P: Pedagogical Grant | O: Other, U: Unknown
**Type
C: Granting Councils | G: Government | F: Foundations | I: UO Internal Funding | O: Other | U: Unknown
Role
PI = Principal Investigator | Co-I = Co-Investigator | Co-PI = Co-Principal Investigator
