Volume 12 (2024)
Volume 11 (2023)
Volume 10 (2022)
Volume 9 (2021)
Volume 8 (2020)
Volume 7 (2019)
Volume 6 (2018)
Volume 5 (2017)
Volume 4 (2016)
Volume 3 (2015)
Volume 2 (2014)
Volume 1 (2013)
H.3. Artificial Intelligence
X-SHAoLIM: Novel Feature Selection Framework for Credit Card Fraud Detection

Sajjad Alizadeh Fard; Hossein Rahmani

Volume 12, Issue 1 , January 2024, , Pages 57-66

https://doi.org/10.22044/jadm.2024.13630.2480

Abstract
  Fraud in financial data is a significant concern for both businesses and individuals. Credit card transactions involve numerous features, some of which may lack relevance for classifiers and could lead to overfitting. A pivotal step in the fraud detection process is feature selection, which profoundly ...  Read More

H.3. Artificial Intelligence
Application of Stacked Ensemble Techniques in Head and Neck Squamous Cell Carcinoma Prognostic Feature Subsets

Damianus Kofi Owusu; Christiana Cynthia Nyarko; Joseph Acquah; Joel Yarney

Volume 12, Issue 1 , January 2024, , Pages 67-81

https://doi.org/10.22044/jadm.2023.12420.2388

Abstract
  Head and neck cancer (HNC) recurrence is ever increasing among Ghanaian men and women. Because not all machine learning classifiers are equally created, even if multiple of them suite very well for a given task, it may be very difficult to find one which performs optimally given different distributions. ...  Read More

H.3.15.3. Evolutionary computing and genetic algorithms
A Hybrid Machine Learning Approach and Genetic Algorithm for Malware Detection

Mahdieh Maazalahi; Soodeh Hosseini

Volume 12, Issue 1 , January 2024, , Pages 95-104

https://doi.org/10.22044/jadm.2024.13895.2503

Abstract
  Detecting and preventing malware infections in systems is become a critical necessity. This paper presents a hybrid method for malware detection, utilizing data mining algorithms such as simulated annealing (SA), support vector machine (SVM), genetic algorithm (GA), and K-means. The proposed method combines ...  Read More

C.3. Software Engineering
Accuracy Improvement in Software Cost Estimation based on Selection of Relevant Features of Homogeneous Clusters

Saba Beiranvand; Mohammad Ali Zare Chahooki

Volume 11, Issue 3 , July 2023, , Pages 453-476

https://doi.org/10.22044/jadm.2023.12750.2429

Abstract
  Software Cost Estimation (SCE) is one of the most widely used and effective activities in project management. In machine learning methods, some features have adverse effects on accuracy. Thus, preprocessing methods based on reducing non-effective features can improve accuracy in these methods. In clustering ...  Read More

F.4.18. Time series analysis
Time Series Clustering based on Aggregation and Selection of Extracted Features

Ali Ghorbanian; Hamideh Razavi

Volume 11, Issue 2 , April 2023, , Pages 303-314

https://doi.org/10.22044/jadm.2023.13089.2449

Abstract
  In time series clustering, features are typically extracted from the time series data and used for clustering instead of directly clustering the data. However, using the same set of features for all data sets may not be effective. To overcome this limitation, this study proposes a five-step algorithm ...  Read More

Efficient Feature Selection Method using Binary Teaching-learning-based Optimization Algorithm

S. Hosseini; M. Khorashadizade

Volume 11, Issue 1 , January 2023, , Pages 29-37

https://doi.org/10.22044/jadm.2023.12497.2400

Abstract
  High dimensionality is the biggest problem when working with large datasets. Feature selection is a procedure for reducing the dimensionality of datasets by removing additional and irrelevant features; the most effective features in the dataset will remain, increasing the algorithms’ performance. ...  Read More

Feature Selection based on Particle Swarm Optimization and Mutual Information

Z. Shojaee; Seyed A. Shahzadeh Fazeli; E. Abbasi; F. Adibnia

Volume 9, Issue 1 , January 2021, , Pages 39-44

https://doi.org/10.22044/jadm.2020.8857.2020

Abstract
  Today, feature selection, as a technique to improve the performance of the classification methods, has been widely considered by computer scientists. As the dimensions of a matrix has a huge impact on the performance of processing on it, reducing the number of features by choosing the best subset of ...  Read More

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

M. Salehi; J. Razmara; Sh. Lotfi

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

https://doi.org/10.22044/jadm.2020.8406.1978

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

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

S. Beigi; M.R. Amin Naseri

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

https://doi.org/10.22044/jadm.2019.7506.1894

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.6.3.2. Feature evaluation and selection
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 , April 2020, , Pages 237-245

https://doi.org/10.22044/jadm.2020.8105.1945

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

H.3. Artificial Intelligence
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 , January 2019, , Pages 59-68

https://doi.org/10.22044/jadm.2018.6489.1763

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
Extracting Predictor Variables to Construct Breast Cancer Survivability Model with Class Imbalance Problem

S. Miri Rostami; M. Ahmadzadeh

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

https://doi.org/10.22044/jadm.2017.5061.1609

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
Intrusion Detection based on a Novel Hybrid Learning Approach

L. khalvati; M. Keshtgary; N. Rikhtegar

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

https://doi.org/10.22044/jadm.2017.979

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

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

F. Karimian; S. M. Babamir

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

https://doi.org/10.22044/jadm.2016.825

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

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

F. Fadaei Noghani; M. Moattar

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

https://doi.org/10.22044/jadm.2016.788

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