1. Development of an Ensemble Multi-stage Machine for Prediction of Breast Cancer Survivability

M. Salehi; J. Razmara; Sh. Lotfi

Volume 8, Issue 3 , Summer 2020, , Pages 371-378

Abstract
  Prediction of cancer survivability using machine learning techniques has become a popular approach in recent years. ‎In this regard, an important issue is that preparation of some features may need conducting difficult and costly experiments while these features have less significant impacts on the ...  Read More

H.6.3.2. Feature evaluation and selection
2. H-BwoaSvm: A Hybrid Model for Classification and Feature Selection of Mammography Screening Behavior Data

E. Enayati; Z. Hassani; M. Moodi

Volume 8, Issue 2 , Spring 2020, , Pages 237-245

Abstract
  Breast cancer is one of the most common cancer in the world. Early detection of cancers cause significantly reduce in morbidity rate and treatment costs. Mammography is a known effective diagnosis method of breast cancer. A way for mammography screening behavior identification is women's awareness evaluation ...  Read More

J.10.3. Financial
3. Credit Card Fraud Detection using Data mining and Statistical Methods

S. Beigi; M.R. Amin Naseri

Volume 8, Issue 2 , Spring 2020, , Pages 149-160

Abstract
  Due to today’s advancement in technology and businesses, fraud detection has become a critical component of financial transactions. Considering vast amounts of data in large datasets, it becomes more difficult to detect fraud transactions manually. In this research, we propose a combined method ...  Read More

H.3. Artificial Intelligence
4. A New Knowledge-Based System for Diagnosis of Breast Cancer by a combination of the Affinity Propagation and Firefly Algorithms

N. Emami; A. Pakzad

Volume 7, Issue 1 , Winter 2019, , Pages 59-68

Abstract
  Breast cancer has become a widespread disease around the world in young women. Expert systems, developed by data mining techniques, are valuable tools in diagnosis of breast cancer and can help physicians for decision making process. This paper presents a new hybrid data mining approach to classify two ...  Read More

F.4.17. Survival analysis
5. Extracting Predictor Variables to Construct Breast Cancer Survivability Model with Class Imbalance Problem

S. Miri Rostami; M. Ahmadzadeh

Volume 6, Issue 2 , Summer 2018, , Pages 263-276

Abstract
  Application of data mining methods as a decision support system has a great benefit to predict survival of new patients. It also has a great potential for health researchers to investigate the relationship between risk factors and cancer survival. But due to the imbalanced nature of datasets associated ...  Read More

C.1. General
6. Intrusion Detection based on a Novel Hybrid Learning Approach

L. khalvati; M. Keshtgary; N. Rikhtegar

Volume 6, Issue 1 , Winter 2018, , Pages 157-162

Abstract
  Information security and Intrusion Detection System (IDS) plays a critical role in the Internet. IDS is an essential tool for detecting different kinds of attacks in a network and maintaining data integrity, confidentiality and system availability against possible threats. In this paper, a hybrid approach ...  Read More

H.3. Artificial Intelligence
7. Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection

F. Fadaei Noghani; M. Moattar

Volume 5, Issue 2 , Summer 2017, , Pages 235-243

Abstract
  Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost ...  Read More

C.3. Software Engineering
8. Evaluation of Classifiers in Software Fault-Proneness Prediction

F. Karimian; S. M. Babamir

Volume 5, Issue 2 , Summer 2017, , Pages 149-167

Abstract
  Reliability of software counts on its fault-prone modules. This means that the less software consists of fault-prone units the more we may trust it. Therefore, if we are able to predict the number of fault-prone modules of software, it will be possible to judge the software reliability. In predicting ...  Read More