All Outputs (7)

Better classifiers for credit scoring : a comparison study between self organizing maps (SOM) and support vector machine (SVM) (2009)
Presentation / Conference
Shahlaii Moghadam, A., Shalbafzadeh, A., & Saraee, M. (2009, December). Better classifiers for credit scoring : a comparison study between self organizing maps (SOM) and support vector machine (SVM). Presented at 3rd International Conference on Communications and information technology, Vouliagmeni, Athens, Greece

Credit scoring has become an increasingly important area for financial institutions. Self Organizing Maps and Support Vector Machine are two techniques of data mining which are used in different applications of businesses. In this paper, we use descr... Read More about Better classifiers for credit scoring : a comparison study between self organizing maps (SOM) and support vector machine (SVM).

Better classifiers for credit scoring: a comparison study between self organizing maps (SOM) and support vector machine (SVM) (2009)
Presentation / Conference
Shahlaii Moghada, A., Shalbafzadeh, A., & Saraee, M. (2009, December). Better classifiers for credit scoring: a comparison study between self organizing maps (SOM) and support vector machine (SVM). Presented at 3rd International Conference on Communications and Information Technology, Vouliagmeni, Athens, Greece

Credit scoring has become an increasingly important area for financial institutions. Self Organizing Maps (SOM) and Support Vector Machine(SVM) are two techniques of data mining which are being used in different applications of businesses. In this p... Read More about Better classifiers for credit scoring: a comparison study between self organizing maps (SOM) and support vector machine (SVM).

Extracting temporal rules from medical data (2009)
Presentation / Conference
Meamarzadeh, H., Khayyambashi, M., & Saraee, M. (2009, November). Extracting temporal rules from medical data. Presented at The 2009 International Conference on Computer Technology and Development, Kota, Kinabalu, Malaysia

Trauma is the main leading cause of death in children; we need a tool to prevent and predict the outcome in these patients. Data mining is the science of extracting the useful information from a large amount of data sets or databases that leads to... Read More about Extracting temporal rules from medical data.

Data mining cardiovascular risk factors (2009)
Presentation / Conference
Kajabadi, A., Saraee, M., & Asgari, S. (2009, October). Data mining cardiovascular risk factors. Presented at International Conference on Application of Information and Communication Technologies, 2009. AICT 2009., Baku, Azerbaija

Nowadays, medical centers collect various data in different diseases. Investigating these data and obtaining useful results and patterns with respect to the diseases are the aims of using these data. Great amount of these data and confusions results... Read More about Data mining cardiovascular risk factors.

Web search personalization: A fuzzy adaptive approach (2009)
Presentation / Conference
Norouzzadeh, M., Bagheri, A., & Saraee, M. (2009, August). Web search personalization: A fuzzy adaptive approach. Presented at 2nd IEEE International Conference on Computer Science and Information Technology, 2009. ICCSIT 2009, Beijing, China,

Today the growing rate of Web data has become so large and this is the reason for turning search engines into the major decision support systems for the Internet. In this paper, a novel and simple approach is proposed to improve Web search. The appro... Read More about Web search personalization: A fuzzy adaptive approach.

Application of self-organizing map to model a machining process (2009)
Presentation / Conference
Saraee, M., Moosavi, S., & Rezapoor, S. (2009, July). Application of self-organizing map to model a machining process. Presented at The 4th European Conference on Intelligent Management Systems in Operations, Salford, UK

Protein contact map prediction based on an ensemble learning method (2009)
Presentation / Conference
Habibi, N., & Saraee, M. (2009, January). Protein contact map prediction based on an ensemble learning method. Presented at International Conference on Computer Engineering and Technology (ICCET 2009),, Singapore

Contact map is the simplified, 2D representation of protein spatial structure. Contact map prediction is an intermediate step to predict protein 3D structure. Ensemble learning-based model is a collection of learners that is more accurate than a sing... Read More about Protein contact map prediction based on an ensemble learning method.