School of Science, Engineering & Environment

Mars image segmentation with most relevant features among wavelet and color features (2015)
Presentation / Conference
Rashno, A., Saraee, M., & Sadri, S. (2015, April). Mars image segmentation with most relevant features among wavelet and color features. Presented at AI & Robotics (IRANOPEN), 2015

Mars rover is a robot which explores the Mars surface, is equipped to front-line Panoramic Camera (Pancam). Automatic processing and segmentation of images taken by Pancam is one of the most important and most significant tasks of Mars rover since th... Read More about Mars image segmentation with most relevant features among wavelet and color features.

PSA : a hybrid feature selection approach for Persian text classification (2015)
Journal Article
Bagheri, A., Saraee, M., & Nadi, S. (2015). PSA : a hybrid feature selection approach for Persian text classification. Journal of computing and security (Online), 1(4), 261-272

In recent decades, as enormous amount of data being accumulated, the number of text documents is increasing vastly. E-mails, web pages, texts, news and articles are only part of this grow. Thus the need for text mining techniques, including automatic... Read More about PSA : a hybrid feature selection approach for Persian text classification.

A novel feature selection method for text classification using association rules and clustering (2014)
Journal Article
Sheydaei, N., Saraee, M., & Shahgholian, A. (2015). A novel feature selection method for text classification using association rules and clustering. Journal of Information Science, 41(1), 3-15. https://doi.org/10.1177/0165551514550143

Readability and accuracy are two important features of a good classifier. For reasons such as acceptable accuracy, rapid training and high interpretability, associative classifiers have been recently used in many categorization tasks. These features... Read More about A novel feature selection method for text classification using association rules and clustering.

Persian sentiment analyzer : a framework based on a novel feature selection method (2014)
Journal Article
feature selection method. International Journal of Artificial Intelligence, 12(2), 115-129

In the recent decade, with the enormous growth of digital content in internet and databases,
sentiment analysis has received more and more attention between information retrieval and
natural language processing researchers. Sentiment analysis aims... Read More about Persian sentiment analyzer : a framework based on a novel feature selection method.

MRAR : mining multi-relation association rules (2014)
Journal Article
Ramezani, R., Saraee, M., & Nematbakhsh, M. (2014). MRAR : mining multi-relation association rules. Journal of computing and security (Online), 1(2), 133-158

In this paper, we introduce a new class of association rules (ARs) named
“Multi-Relation Association Rules” which in contrast to primitive ARs (that
are usually extracted from multi-relational databases), each rule item consists
of one entity and... Read More about MRAR : mining multi-relation association rules.

A fuzzy method for discovering cost-effective actions from data (2014)
Journal Article
Kalanat, N., Shamsinejadbabaki, P., & Saraee, M. (2014). A fuzzy method for discovering cost-effective actions from data. Journal of Intelligent and Fuzzy Systems, 28(2), 757-765. https://doi.org/10.3233/IFS-141357

Data mining techniques are often confined to the delivery of frequent patterns and stop short of suggesting how to act on these patterns for business decision-making. They require human experts to post-process the discovered patterns manually. Theref... Read More about A fuzzy method for discovering cost-effective actions from data.

ADM-LDA : an aspect detection model based on topic modelling using the structure of review sentences (2014)
Journal Article
Bagheri, A., Saraee, M., & de Jong, F. (2014). ADM-LDA : an aspect detection model based on topic modelling using the structure of review sentences. Journal of Information Science, 40(5), 621-636. https://doi.org/10.1177/0165551514538744

Probabilistic topic models are statistical methods whose aim is to discover the latent structure in a large collection of documents. The intuition behind topic models is that, by generating documents by latent topics, the word distribution for each t... Read More about ADM-LDA : an aspect detection model based on topic modelling using the structure of review sentences.

SWApriori : a new approach to mining association rules from semantic web data (2014)
Journal Article

With the introduction and standardization of the semantic web as the third generation of the Web, this technology has attracted and received more human attention than ever and thus the amount of semantic web data is constantly growing. These semantic... Read More about SWApriori : a new approach to mining association rules from semantic web data.

Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews (2013)
Journal Article
Bagheri, A., Saraee, M., & de Jong, F. (2013). Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews. Knowledge-Based Systems, 52(2013), 201-213. https://doi.org/10.1016/j.knosys.2013.08.011

With the rapid growth of user-generated content on the internet, automatic sentiment analysis of online customer reviews has become a hot research topic recently, but due to variety and wide range of products and services being reviewed on the intern... Read More about Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews.

Outputs (187)