Behrouz Minaei-Bidgoli‬

Iran University of Science and Technology

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Hamïd Parvïn

Islamic Azad University, Tehran

Hosein Alizadeh

Iran University of Science and Technology

Mahdi Nasiri

Shahid Beheshti University of Medical Sciences

William F. Punch

Michigan State University

Rohollah Omidvar

Islamic Azad University of Shiraz

Behrooz Janfada

Zhejiang Sci-Tech University

Majid Asgari-Bidhendi

Iran University of Science and Technology

Ali Hadian

Imperial College London

Hamed Aliyari

The University of Texas Southwestern Medical Center

Masoomeh Kazemi

University of North Carolina at Chapel Hill

Cooperative Institutions

Iran University of Science and Technology

183

Islamic Azad University, Tehran

University of Tehran

Islamic Azad University, Science and Research Branch

Amirkabir University of Technology

Tehran University of Medical Sciences

University of Qom

Tarbiat Modares University

Ferdowsi University of Mashhad

Sharif University of Technology

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Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence

Expert Systems with Applications (2010)

Hamid Reza Qodmanan Mahdi Nasiri Behrouz Minaei-Bidgoli‬

10.1016/j.eswa.2010.06.060

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Citations (131)

Elite fuzzy clustering ensemble based on clustering diversity and quality measures

Applied Intelligence (2018)

Ali Bagherinia Behrouz Minaei-Bidgoli‬Mehdi Hossinzadeh Hamïd Parvïn

Consensus clustering

Hierarchical clustering

10.1007/s10489-018-1332-x

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Citations (57)

ParsEL 1.0: Unsupervised Entity Linking in Persian Social Media Texts

Majid Asgari-Bidhendi Farzane Fakhrian Behrouz Minaei-Bidgoli‬

Social media users have exponentially increased in recent years, and social media data has become one of the most populated repositories of data in the world. Natural language text is one of the main portions of this data. However, this textual data contains many entities, which increases the ambiguity of the data. Entity linking targets finding entity mentions and linking them to their corresponding entities in an external dataset. Recently, FarsBase has been introduced as the first Persian knowledge graph, containing almost 750,000 entities. In this study, we propose ParsEL, the first unsupervised end-to-end entity linking system specially designed for the Persian language, and utilizes contextual and graph-based features to rank the candidate entities. To evaluate the proposed approach, we publish the first entity linking dataset for the Persian language, created by crawling social media text from some popular Telegram channels and contains 67,595 tokens. The results show ParsEL records 86.94% f-score for the introduced dataset, and it is comparable with one other entity linking system which supports the Persian language.

Crawling

Publication

Rank (graph theory)

Entity linking

10.1109/ikt51791.2020.9345631

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Citations (2)

Multi-View Learning for Web Spam Detection

arXiv (Cornell University) (2013)

Ali Hadian Behrouz Minaei-Bidgoli‬

Spam pages are designed to maliciously appear among the top search results by excessive usage of popular terms. Therefore, spam pages should be removed using an effective and efficient spam detection system. Previous methods for web spam classification used several features from various information sources (page contents, web graph, access logs, etc.) to detect web spam. In this paper, we follow page-level classification approach to build fast and scalable spam filters. We show that each web page can be classified with satisfiable accuracy using only its own HTML content. In order to design a multi-view classification system, we used state-of-the-art spam classification methods with distinct feature sets (views) as the base classifiers. Then, a fusion model is learned to combine the output of the base classifiers and make final prediction. Results show that multi-view learning significantly improves the classification performance, namely AUC by 22%, while providing linear speedup for parallel execution.

Spamdexing

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ACUT: An Associative Classifier Approach to Unknown Word POS Tagging

Communications in computer and information science (2014)

Mohammad Hossein Elahimanesh Behrouz Minaei-Bidgoli‬Fateme Kermani

Associative property

Part-of-Speech Tagging

10.1007/978-3-319-10849-0_26

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Persian FrameNet: a Novel Approach to build FrameNet in the Persian Language Applicable to Islamic Context

Mohammad Hossein Shojaei Baghini Behrooz Janfada Behrouz Minaei-Bidgoli‬

FrameNet is a lexical research project that produces a glossary containing very detailed information about syntax (semantic relationships of specific English words such as verbs, nouns, and adjectives). Both human and computer users can use this glossary, so it has become an essential lexical knowledge base for use in fields such as natural language processing and vocabulary semantics. Due to the importance of FrameNet in natural language processing, there is also a need to create FrameNet in languages other than English. There have been few attempts to produce a FrameNet in Persian so far. On the other hand, the existence of an efficient Persian FrameNet can help with the processing of natural language in Islamic texts. In this paper, we present a novel approach to building our first version of the Persian FrameNet. We will then show that although this version is in its infancy, it can be comparable to the FrameNet of some other important languages in terms of frame coverage.

FrameNet

Glossary

10.1109/cfis49607.2020.9238674

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A New Design for Knowledge Visualization based on User's Understanding of Wiki data

Mohsen Khoshgoftar Behrouz Minaei-Bidgoli‬Ahmad Faraahi

Summary This paper represents a method for the knowledge visualization for the technical words in Newton’s Telecom Dictionary in order to make users understand the intended topic and improve the results of search for resources in wikis by Google search engine. The WordNet dictionary and the Encyclopedia Wikipedia are used as the sources of knowledge acquiring. The new design of visualization named CloudViz Knowledge, in which there is a hierarchy of knowledge tree for the intended word along with its various levels of meaning, is also introduced here. Understanding the meaning of words and gaining mastery of the related knowledge assist users to achieve a desired research with more related results in a short period of time.

Encyclopedia

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Case study: Data Mining of Associate Degree Accepted Candidates by Modular Method

arXiv (Cornell University) (2014)

Behrouz Minaei-Bidgoli‬Maryam Nazaridoust

Since about 10 years ago, University of Applied Science and Technology (UAST) in Iran has admitted students in discontinuous associate degree by modular method, so that almost 100,000 students are accepted every year. Although the first aim of holding such courses was to improve scientific and skill level of employees, over time a considerable group of unemployed people have been interested to participate in these courses. According to this fact, in this paper, we mine and analyze a sample data of accepted candidates in modular 2008 and 2009 courses by using unsupervised and supervised learning paradigms. In the first step, by using unsupervised paradigm, we grouped (clustered) set of modular accepted candidates based on their student status and labeled data sets by three classes so that each class somehow shows educational and student status of modular accepted candidates. In the second step, by using supervised and unsupervised algorithms, we generated predicting models in 2008 data sets. Then, by making a comparison between performances of generated models, we selected predicting model of association rules through which some rules were extracted. Finally, this model is executed for Test set which includes accepted candidates of next course then by evaluation of results, the percentage of correctness and confidentiality of obtained results can be viewed.

Sample (material)

Degree (music)

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Providing a new validation index to be used in interval type 2 fuzzy C-means (IT-2 FCM) algorithm

Journal of Operational Research In Its Applications ( Applied Mathematics ) - Lahijan Azad University (2019)

Iman Mesgari Vahid Reza Salamat Behrouz Minaei-Bidgoli‬

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Prediction of Stock Market Using an Ensemble Learning-based Intelligent Model

Industrial Engineering & Management Systems (2018)

Mohammad-Taghi Faghihi-Nezhad Behrouz Minaei-Bidgoli‬

AI-based models have shown that stock market is predictable despite its uncertainty and fluctuating nature. Research in this field has further dealt with predicting the next step price amount and less attention has been paid to the prediction of the next movement of price. However, in practice, the necessary requisite for decision-making and use of the results of prediction lies in considering the predictable trend of stock movement along with predicting stock price. Considering the widespread search in the literature on the matter, this paper takes into account, for the first time, two criteria of direction and price simultaneously for the prediction of the stock price. The proposed model has two stages and is developed based on ensemble learning and meta-heuristic optimization algorithms. The first stage predicts the direction of the next price movement. At the second stage, such prediction and other input variables create a new training dataset and the stock price is predicted. At each stage, in order to optimize the results, genetic algorithm (GA) optimization and particle swarm optimization (PSO) are applied. Evaluation of the results, on the real data of stock price, indicates that the proposed model has higher accuracy than other models used in the literature.

Ensemble Learning

10.7232/iems.2018.17.3.479

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Citations (13)