A distributed joint sentiment and topic modeling using spark for big opinion mining (2017)
Book Chapter
Zahedi, E., Saraee, M., & Baniasadi, Z. (2017). A distributed joint sentiment and topic modeling using spark for big opinion mining. In Iranian Conference on Electrical Engineering (ICEE), 2017 (1475-1480). IEEE. https://doi.org/10.1109/IranianCEE.2017.7985276

Opinion data are produced rapidly by a large and uncontrolled number of opinion holders in different domains (public, business, politic and etc). The volume, variety and velocity of such data requires an opinion mining model to be also adopted with t... Read More about A distributed joint sentiment and topic modeling using spark for big opinion mining.

Preface to the workshop on cost sensitive data mining (2012)
Book Chapter
Vadera, S., Saraee, M., & Lomax, S. (2012). Preface to the workshop on cost sensitive data mining. In J. Vreeken, C. Ling, M. Zaki, A. Siebes, J. Yu, B. Goethals, …X. Wu (Eds.), The 12th IEEE International Conference on Data Mining : Workshops. IEEE. https://doi.org/10.1109/ICDMW.2012.148

Much of the early work on data mining concentrated on developing algorithms that focused on classification accuracy. A more challenging and practical problem is to devise algorithms that learn rules or associations that optimize income and take bette... Read More about Preface to the workshop on cost sensitive data mining.

Proximity user identification using correlogram (2010)
Book Chapter
Shahidi, S., Mazrooei, P., Esfahani, N., & Saraee, M. (2010). Proximity user identification using correlogram. In Intelligent Information Processing (343-351). Berlin: Springer -Velag

This paper represents a technique, applying user action patterns in order to distinguish between users and identify them. In this method, users’ actions sequences are mapped to numerical sequences and each user's profile is generated using autocorre... Read More about Proximity user identification using correlogram.

Privacy-preserving data mining in peer to peer networks (2010)
Book Chapter
Hussain, I., Irakleous, M., Siddiqi, M., & Saraee, M. (2010). Privacy-preserving data mining in peer to peer networks. In proceedings from the Annual International Conference on Data Analysis, Data Quality and Metadata Management. GSTF

In recent years, privacy-preserving data mining has been studied extensively, due to the wide increase of sensitive information on the internet. A number of algorithms and procedures have been designed, some of which are yet to be implemented, but a... Read More about Privacy-preserving data mining in peer to peer networks.

A new and improved skin detection method using mixed color space (2009)
Book Chapter
Aznaveh, M., Mirzaei, H., Roshan, E., & Saraee, M. (2009). A new and improved skin detection method using mixed color space. In Human-Computer Systems Interaction (471-480). Berlin: Springer. https://doi.org/10.1007/978-3-642-03202-8_37

In this paper a new and robust approach of skin detection is proposed. In the previous proposed system, we introduced a method for skin detection based on RGB vector space. An extended and modified approach based on a mixed color space is presented.... Read More about A new and improved skin detection method using mixed color space.

A new linear appearance-based method in face recognition (2008)
Book Chapter
Hajiarbabi, M., Askari, J., Sadri, S., & Saraee, M. (2008). A new linear appearance-based method in face recognition. In Advances in Communication Systems and Electrical Engineering (579-587). Springer. https://doi.org/10.1007/978-0-387-74938-9_39

Human identification recognition has attracted scientists for many years. During these years, and due to increases in terrorism, the need for such systems has increased much more. The most important biometric systems that have been used during these... Read More about A new linear appearance-based method in face recognition.

Iris disease classifying using neuro-fuzzy medical diagnosis machine
Book Chapter
Moein, S., Saraee, M., & Moein, M. Iris disease classifying using neuro-fuzzy medical diagnosis machine. In The Sixth International Symposium on Neural Networks (ISNN 2009) (359-368). Springer Berlin / Heidelberg,. https://doi.org/10.1007/978-3-642-01216-7_38

Disease diagnosis is an essential task in the medical world. The use of computers in the practice of medicine is becoming more and more crucial. In this paper, we propose an intelligent system to help us diagnose the Iris disease. This system is base... Read More about Iris disease classifying using neuro-fuzzy medical diagnosis machine.