G.3.9. Database Applications
1. 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

Abstract
  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.3.2.3. Decision support
2. Analyzing and Investigating the Use of Electronic Payment Tools in Iran using Data Mining Techniques

F. Moslehi; A.R. Haeri; A.R. Moini

Volume 6, Issue 2 , Summer 2018, , Pages 417-437

Abstract
  In today's world, most financial transactions are carried out using done through electronic instruments and in the context of the Information Technology and Internet. Disregarding the application of new technologies at this field and sufficing to traditional ways, will result in financial loss and customer ...  Read More

G.3.5. Systems
3. Assessment Methodology for Anomaly-Based Intrusion Detection in Cloud Computing

M. Rezvani

Volume 6, Issue 2 , Summer 2018, , Pages 387-397

Abstract
  Cloud computing has become an attractive target for attackers as the mainstream technologies in the cloud, such as the virtualization and multitenancy, permit multiple users to utilize the same physical resource, thereby posing the so-called problem of internal facing security. Moreover, the traditional ...  Read More

H.6.4. Clustering
4. 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

Abstract
  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.6.3.2. Feature evaluation and selection
5. Feature extraction of hyperspectral images using boundary semi-labeled samples and hybrid criterion

M. Imani; H. Ghassemian

Volume 5, Issue 1 , Winter 2017, , Pages 39-53

Abstract
  Feature extraction is a very important preprocessing step for classification of hyperspectral images. The linear discriminant analysis (LDA) method fails to work in small sample size situations. Moreover, LDA has poor efficiency for non-Gaussian data. LDA is optimized by a global criterion. Thus, it ...  Read More

J.10.3. Financial
6. The application of data mining techniques in manipulated financial statement classification: The case of turkey

G. Ozdagoglu; A. Ozdagoglu; Y. Gumus; G. Kurt Gumus

Volume 5, Issue 1 , Winter 2017, , Pages 67-77

Abstract
  Predicting financially false statements to detect frauds in companies has an increasing trend in recent studies. The manipulations in financial statements can be discovered by auditors when related financial records and indicators are analyzed in depth together with the experience of auditors in order ...  Read More

H.6.3.2. Feature evaluation and selection
7. Feature reduction of hyperspectral images: Discriminant analysis and the first principal component

Maryam Imani; Hassan Ghassemian

Volume 3, Issue 1 , Winter 2015, , Pages 1-9

Abstract
  When the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. In this paper, we propose a supervised feature extraction method based on discriminant analysis (DA) which uses the first principal component (PC1) to weight the scatter ...  Read More

8. Data mining for decision making in engineering optimal design

Amir Mosavi

Volume 2, Issue 1 , Winter 2014, , Pages 7-14

Abstract
  Often in modeling the engineering optimization design problems, the value of objective function(s) is not clearly defined in terms of design variables. Instead it is obtained by some numerical analysis such as FE structural analysis, fluid mechanic analysis, and thermodynamic analysis, etc. Yet, the ...  Read More