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


  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.3.7. Learning
Tree Bark Classification using Color-improved Local Quinary Pattern and Stacked MEETG

Laleh Armi; Elham Abbasi

Volume 11, Issue 3 , July 2023, , Pages 391-405


  In this paper, we propose an innovative classification method for tree bark classification and tree species identification. The proposed method consists of two steps. In the first step, we take the advantages of ILQP, a rotationally invariant, noise-resistant, and fully descriptive color texture feature ...  Read More

An Ensemble Convolutional Neural Networks for Detection of Growth Anomalies in Children with X-ray Images

H. Sarabi Sarvarani; F. Abdali-Mohammadi

Volume 10, Issue 4 , November 2022, , Pages 479-492


  Bone age assessment is a method that is constantly used for investigating growth abnormalities, endocrine gland treatment, and pediatric syndromes. Since the advent of digital imaging, for several decades the bone age assessment has been performed by visually examining the ossification of the left hand, ...  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


  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.6.4. Clustering
Entropy-based Consensus for Distributed Data Clustering

M. Owhadi-Kareshki; M.R. Akbarzadeh-T.

Volume 7, Issue 4 , November 2019, , Pages 551-561


  The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality ...  Read More

H.6.3.1. Classifier design and evaluation
Ensemble-based Top-k Recommender System Considering Incomplete Data

M. Moradi; J. Hamidzadeh

Volume 7, Issue 3 , July 2019, , Pages 393-402


  Recommender systems have been widely used in e-commerce applications. They are a subclass of information filtering system, used to either predict whether a user will prefer an item (prediction problem) or identify a set of k items that will be user-interest (Top-k recommendation problem). Demanding sufficient ...  Read More