Faculty of Computer Science and Information Systems University of Malaya, Kuala Lumpur, Malaysia
Field of Research; Computer Science (Educational Data Mining)
Thesis- A soft set approach to evaluate academic performance based on student’s academic and demographic data
M.Sc, University of Staffordshire, UK
Majors: Computer Science
Dissertation Title: A Framework for mining the top call drivers as experienced in technical contact centers Published paper
B.Sc, Panjab University, Chandigarh, India
Dutt, A., Ismail, MA., Herawan, T., A Systematic Review on Educational Data Mining (2017). IEEE Access.
Sardareh, S. A., Aghabozorgi, S., & Dutt, A. (2015). Applying Clustering Approach to Analyze Reflective Dialogues and Students’ Problem Solving Ability. Indian Journal of Science and Technology, 8(11).
Dutt, A., Aghabozorgi, S., Maizatul, A.B.I., Mahroeian, H. (2014). Clustering Algorithms Applied in Educational Data Mining. International Journal of Information and Electronics Engineering (IJIEE), 5(2).
Dutt, A., Aghabozorgi. (2014). A Framework for mining top call drivers as experienced in technical contact centres to facilitate self help portals. 6th International Conference on Education and Information Management (ICEIM-2014), Kuala Lumpur, Malaysia.
Aghabozorgi, S., Mahroeian, H., Dutt, A., Wah, T.Y., Herawan, T.. (2014). An Approachable Analytical Study on Big Educational Data Mining. In: Murgante, B., Misra, S., Rocha, A.M.A.C., Torre, C., Rocha, J.G., Falcão, M.I., Taniar, D., Apduhan, B.O., Gervasi, O. Computational Science and Its Applications (ICSSA). Springer International Publishing. 721-737.
Sardareh, S. A., Aghabozorgi, S., & Dutt, A. (2014). Reflective Dialogues and Students’ Problem Solving Ability Analysis Using Clustering. Paper presented at the The 3rd International Conference on Computer Engineering and Mathematical Sciences (ICCEMS 2014), Langkawi, Malaysia.
I serve as a reviewer to many international journals. Peer-review is a voluntary service and one is not paid for it.
- IEEE Access Journal (ISSN: 2169-3536)
IEEE Access is an Open Access journal. It is a Q1 (JCR year 2016) journal in Computer Science with an impact factor of 2.95. It is published by IEEE, USA.
- IEEE Transactions on Learning Technologies Journal (ISSN: 1939-1382)
IEEE Transactions on Learning Technologies is a Q2 (JCR year 2016) journal in Computer Science and Interdisciplinary Applications with an impact factor of 2.083. It is published by IEEE Computer Society from California, USA. It publishes 4 issues/year
- Kybernetes Journal (ISSN: 0368-492X)
Kybernetes is a Q4 (JCR year 2016) journal in Computer Science and Cybernetics with an impact factor of 0.811. It is published by Emerald group at Yorkshire, England and publishes 10 issues/year.
Data Analyst (Jun 2015 Apr 2016)
Graduate Research Assistant (Data Science)(Jun 2014 Jun 2015)
Teaching Assistant (Data Science)(04/06/2012 until 01/03/2013)
Technical Consultant Level 2 (14/12/2009 until 20/02/2011)
Technical Consultant Level 1 (18/07/2006 until 16/12/2009)
Project Assistant (12/04/2004 until 11/09/2006)
Senior Lecturer (Data Science) (02/12/2002 until 31/03/2004)
Lecturer (Data Science) (02/07/1999 until 30/11/2002)
- Python 2.7
Project: Predicting employment related factors in Malaysia- A regression analysis approach
Project duration: February 2017
Objective: Determine the factors which contribute to accurately predicting unemployment rate from historical statistical data on labour force data in Malaysia.
Deliverable: Used the labor force data from the Department of Statistics, Malaysia and performed exploratory, inferential and predictive data analysis. The findings are listed on my research blog and the R code is listed on GitHub.
Project: Predicting rubber plantation yield- A regression analysis approach
Project duration: February 2017
Objective: Determine the factors which contribute to accurately predicting high rubber yield per kg based on historical rubber plantation data.
Project: House price prediction using advanced regression techniques
Project duration: September-November 2016
Objective: Analyze the dataset to improve upon feature engineering and advanced regression algorithms like random forests, gradient boosting with xgboost
Project: Exploratory data analysis of the GapMinder dataset
Project duration: February 2016
Objective: Analyze the dataset to determine the causes of breast cancer in women.