H.5. Image Processing and Computer Vision
1. Video Abstraction in H.264/AVC Compressed Domain

A. R. Yamghani; F. Zargari

Volume 7, Issue 4 , Autumn 2019, , Pages 521-535

  Video abstraction allows searching, browsing and evaluating videos only by accessing the useful contents. Most of the studies are using pixel domain, which requires the decoding process and needs more time and process consuming than compressed domain video abstraction. In this paper, we present a new ...  Read More

H.5.7. Segmentation
2. Image Segmentation using Improved Imperialist Competitive Algorithm and a Simple Post-processing

V. Naghashi; Sh. Lotfi

Volume 7, Issue 4 , Autumn 2019, , Pages 507-519

  Image segmentation is a fundamental step in many of image processing applications. In most cases the image’s pixels are clustered only based on the pixels’ intensity or color information and neither spatial nor neighborhood information of pixels is used in the clustering process. Considering ...  Read More

H.6.2.2. Fuzzy set
3. Developing a Course Recommender by Combining Clustering and Fuzzy Association Rules

Sh. Asadi; Seyed M. b. Jafari; Z. Shokrollahi

Volume 7, Issue 2 , Spring 2019, , Pages 249-262

  Each semester, students go through the process of selecting appropriate courses. It is difficult to find information about each course and ultimately make decisions. The objective of this paper is to design a course recommender model which takes student characteristics into account to recommend appropriate ...  Read More

G.3.9. Database Applications
4. Using Combined Descriptive and Predictive Methods of Data Mining for Coronary Artery Disease Prediction: a Case Study Approach

M. Shamsollahi; A. Badiee; M. Ghazanfari

Volume 7, Issue 1 , Winter 2019, , Pages 47-58

  Heart disease is one of the major causes of morbidity in the world. Currently, large proportions of healthcare data are not processed properly, thus, failing to be effectively used for decision making purposes. The risk of heart disease may be predicted via investigation of heart disease risk factors ...  Read More

H.3. Artificial Intelligence
5. Extracting Prior Knowledge from Data Distribution to Migrate from Blind to Semi-Supervised Clustering

Z. Sedighi; R. Boostani

Volume 6, Issue 2 , Summer 2018, , Pages 287-295

  Although many studies have been conducted to improve the clustering efficiency, most of the state-of-art schemes suffer from the lack of robustness and stability. This paper is aimed at proposing an efficient approach to elicit prior knowledge in terms of must-link and cannot-link from the estimated ...  Read More

B. Computer Systems Organization
6. Target Tracking Based on Virtual Grid in Wireless Sensor Networks

F. Hoseini; A. Shahbahrami; A. Yaghoobi Notash

Volume 6, Issue 2 , Summer 2018, , Pages 313-319

  One of the most important and typical application of wireless sensor networks (WSNs) is target tracking. Although target tracking, can provide benefits for large-scale WSNs and organize them into clusters but tracking a moving target in cluster-based WSNs suffers a boundary problem. The main goal of ...  Read More

H.6.4. Clustering
7. Grouping Objects to Homogeneous Classes Satisfying Requisite Mass

M. Manteqipour; A.R. Ghaffari Hadigheh; R. Mahmoodvand; A. Safari

Volume 6, Issue 1 , Winter 2018, , Pages 163-175

  Grouping datasets plays an important role in many scientific researches. Depending on data features and applications, different constrains are imposed on groups, while having groups with similar members is always a main criterion. In this paper, we propose an algorithm for grouping the objects with random ...  Read More

H.6.4. Clustering
8. Improved COA with Chaotic Initialization and Intelligent Migration for Data Clustering

M. Lashkari; M. Moattar

Volume 5, Issue 2 , Summer 2017, , Pages 293-305

  A well-known clustering algorithm is K-means. This algorithm, besides advantages such as high speed and ease of employment, suffers from the problem of local optima. In order to overcome this problem, a lot of studies have been done in clustering. This paper presents a hybrid Extended Cuckoo Optimization ...  Read More

H.3.8. Natural Language Processing
9. Comparing k-means clusters on parallel Persian-English corpus

A. Khazaei; M. Ghasemzadeh

Volume 3, Issue 2 , Summer 2015, , Pages 203-208

  This paper compares clusters of aligned Persian and English texts obtained from k-means method. Text clustering has many applications in various fields of natural language processing. So far, much English documents clustering research has been accomplished. Now this question arises, are the results of ...  Read More

Timing analysis
10. Fuzzy clustering of time series data: A particle swarm optimization approach

Z. Izakian; M. Mesgari

Volume 3, Issue 1 , Winter 2015, , Pages 39-46

  With rapid development in information gathering technologies and access to large amounts of data, we always require methods for data analyzing and extracting useful information from large raw dataset and data mining is an important method for solving this problem. Clustering analysis as the most commonly ...  Read More