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)

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The Journal of Artificial Intelligence & Data Mining (JAIDM) is a scientific journal that aims to develop the international exchange of scientific and technical information in all areas of Artificial Intelligence and Data Mining.

 All manuscripts with significant research results in the scope of the journal are welcome if they are not published or not being considered for publication elsewhere.

Journal of Artificial Intelligence & Data Mining appears quarterly considering the increasing importance of rapid, effective, international communication. JAIDM offers:

  • Publication within a short period after acceptance
  • On-line publication in advance of the printed journal
  • One journal copy  will be sent to the corresponding author
  • Printing, Processing and Delivery without any charges

Topics of interest include, but are not limited to, the following:

  • Artificial Intelligence Algorithms, Tools & Applications
  • Data Mining and Machine Learning Tools
  • Semantic Web Techniques and Technologies
  • Soft computing theory and applications
  • Web Intelligence Applications & Search
  • Bioinformatics
  • Natural Language Processing
  • Computer Vision and Image Processing
  • Speech Understanding
  • Fuzzy Logic
  • Information Retrieval
  • Intelligent System Architectures
  • Knowledge-based/ Expert Systems
  • Automatic Control
  • Neural Networks
  • Parallel Processing
  • Pattern Recognition
  •  Software & Hardware Architectures

All accepted papers will be checked by iThenticate against plagiarism. 

All type papers published by JAIDM are made freely and permanently accessible online immediately upon publication. JAIDM is an "Open access" publishing allows an immediate, world-wide, barrier-free, open access to the full text of research papers, which is in the best interests of the scientific community.

High visibility for maximum global exposure with open access publishing model rigorous peer review (blind peer-review) of research papers prompt faster publication.

JAIDM has no publication charges and no submission fees.

All corresponding authors of each manuscript should be download "COPYRIGHT RELEASE FORM" from above this page then complete and sign this form by all authors and submit this form with all mandatory files which mentioned in bellow. By signing this form, copyright transfer to JAIDM.

Submission of a manuscript implies that:

1) The work described has not been published before (except in the form of an abstract or as part of a published lecture, review, or thesis).

2) It is not under consideration for publication elsewhere.

3) Its publication has been approved by all coauthors, if any, as well as by the responsible authorities at the institute where the work has been carried out.

4) Authors agree to automatic transfer of the copyright to the publisher, if and when their manuscript is accepted for publication.

5) The manuscript will not be published elsewhere.


JAIDM respect all aspects of publication ethics of the Committee on Publication Ethics (COPE). COPE is a forum for editors and publishers of peer reviewed journals to discuss all aspects of publication ethics. COPE provides advice to editors and publishers on all aspects of publication ethics and, in particular, how to handle cases of research and publication misconduct. COPE does not investigate individual cases but encourages editors to ensure that cases are investigated by the appropriate authorities (usually a research institution or employer).


Original/Review Paper
A Bi-objective Virtual-force Local Search PSO Algorithm for Improving Sensing Deployment in Wireless Sensor Network

Vahid Kiani; Mahdi Imanparast

Volume 11, Issue 1 , January 2023, Pages 1-12


  In this paper, we present a bi-objective virtual-force local search particle swarm optimization (BVFPSO) algorithm to improve the placement of sensors in wireless sensor networks while it simultaneously increases the coverage rate and preserves the battery energy of the sensors. Mostly, sensor nodes ...  Read More

Original/Review Paper
An Efficient XCS-based Algorithm for Learning Classifier Systems in Real Environments

Ali Yousefi; Kambiz Badie; Mohammad Mehdi Ebadzadeh; Arash Sharifi

Volume 11, Issue 1 , January 2023, Pages 13-27


  Recently, learning classifier systems are used to control physical robots, sensory robots, and intelligent rescue systems. The most important challenge in these systems, which are models of real environments, is its non-markov quality. Therefore, it is necessary to use memory to store system states in ...  Read More

Original/Review Paper
Efficient Feature Selection Method using Binary Teaching-learning-based Optimization Algorithm

S. Hosseini; M. Khorashadizade

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


  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

Original/Review Paper
FEEM: A Flexible Model based on Artificial Intelligence for Software Effort Estimation

Amin Moradbeiky

Volume 11, Issue 1 , January 2023, Pages 39-51


  Managing software projects due to its intangible nature is full of challenges when predicting the effort needed for development. Accordingly, there exist many studies with the attempt to devise models to estimate efforts necessary in developing software. According to the literature, the accuracy of estimator ...  Read More

Technical Paper
Adaptive Pruning of Convolutional Neural Network

S. Ahmadluei; K. Faez; B. Masoumi

Volume 11, Issue 1 , January 2023, Pages 53-67


  Deep convolutional neural networks (CNNs) have attained remarkable success in numerous visual recognition tasks. There are two challenges when adopting CNNs in real-world applications: a) Existing CNNs are computationally expensive and memory intensive, impeding their use in edge computing; b) there ...  Read More

Original/Review Paper
Efficient Stance Ordering to Improve Rumor Veracity Detection

Z. MohammadHosseini; A. Jalaly Bidgoly

Volume 11, Issue 1 , January 2023, Pages 69-76


  Social media is an inseparable part of human life, although published information through social media is not always true. Rumors may spread easily and quickly in the social media, hence, it is vital to have a tool for rumor veracity detection. Papers already proved that users’ stance is an important ...  Read More

Original/Review Paper
Learning a Nonlinear Combination of Generalized Heterogeneous Classifiers

M. Rahimi; A. A. Taheri; H. Mashayekhi

Volume 11, Issue 1 , January 2023, Pages 77-93


  Finding an effective way to combine the base learners is an essential part of constructing a heterogeneous ensemble of classifiers. In this paper, we propose a framework for heterogeneous ensembles, which investigates using an artificial neural network to learn a nonlinear combination of the base classifiers. ...  Read More

Original/Review Paper
Automatic Post-editing of Hierarchical Attention Networks for Improved Context-aware Neural Machine Translation

M. M. Jaziriyan; F. Ghaderi

Volume 11, Issue 1 , January 2023, Pages 95-102


  Most of the existing neural machine translation (NMT) methods translate sentences without considering the context. It is shown that exploiting inter and intra-sentential context can improve the NMT models and yield to better overall translation quality. However, providing document-level data is costly, ...  Read More

Original/Review Paper H.3. Artificial Intelligence
A Reinforcement Learning-based Encoder-Decoder Framework for Learning Stock Trading Rules

M. Taghian; A. Asadi; R. Safabakhsh

Volume 11, Issue 1 , January 2023, Pages 103-118


  The quality of the extracted features from a long-term sequence of raw prices of the instruments greatly affects the performance of the trading rules learned by machine learning models. Employing a neural encoder-decoder structure to extract informative features from complex input time-series has proved ...  Read More

Original/Review Paper
A Comparison of CQT Spectrogram with STFT-based Acoustic Features in Deep Learning-based Synthetic Speech Detection

P. Abdzadeh; H. Veisi

Volume 11, Issue 1 , January 2023, Pages 119-129


  Automatic Speaker Verification (ASV) systems have proven to bevulnerable to various types of presentation attacks, among whichLogical Access attacks are manufactured using voiceconversion and text-to-speech methods. In recent years, there has beenloads of work concentrating on synthetic speech detection, ...  Read More

Original/Review Paper
Investigating Revenue Smoothing Thresholds That Affect Bank Credit Scoring Models: An Iranian Bank Case Study

Seyed Mahdi Sadatrasoul; Omid Mahdi Ebadati; Amir Amirzadeh Irani

Volume 11, Issue 1 , January 2023, Pages 131-148


  Companies have different considerations for using smoothing in their financial statements, including annual general meeting, auditing, Regulatory and Supervisory institutions and shareholders requirements. Smoothing is done based on the various possible and feasible choices in identifying company’s ...  Read More

Original/Review Paper
A New Adaptive Approach for Efficient Energy Consumption in Edge Computing

H. Morshedlou; A.R. Tajari

Volume 11, Issue 1 , January 2023, Pages 149-159


  Edge computing is an evolving approach for the growing computing and networking demands from end devices and smart things. Edge computing lets the computation to be offloaded from the cloud data centers to the network edge for lower latency, security, and privacy preservation. Although energy efficiency ...  Read More

Credit scoring in banks and financial institutions via data mining techniques: A literature review

Seyed Mahdi sadatrasoul; Mohammadreza gholamian; Mohammad Siami; Zeynab Hajimohammadi

Volume 1, Issue 2 , July 2013, , Pages 119-129


  This paper presents a comprehensive review of the works done, during the 2000–2012, in the application of data mining techniques in Credit scoring. Yet there isn’t any literature in the field of data mining applications in credit scoring. Using a novel research approach, this paper investigates ...  Read More

H.5. Image Processing and Computer Vision
Multi-Focus Image Fusion in DCT Domain using Variance and Energy of Laplacian and Correlation Coefficient for Visual Sensor Networks

M. Amin-Naji; A. Aghagolzadeh

Volume 6, Issue 2 , July 2018, , Pages 233-250


  The purpose of multi-focus image fusion is gathering the essential information and the focused parts from the input multi-focus images into a single image. These multi-focus images are captured with different depths of focus of cameras. A lot of multi-focus image fusion techniques have been introduced ...  Read More

Outlier Detection in Wireless Sensor Networks Using Distributed Principal Component Analysis

Mohammad Ahmadi Livani; mahdi Abadi; Meysam Alikhany; Meisam Yadollahzadeh Tabari

Volume 1, Issue 1 , March 2013, , Pages 1-11


  Detecting anomalies is an important challenge for intrusion detection and fault diagnosis in wireless sensor networks (WSNs). To address the problem of outlier detection in wireless sensor networks, in this paper we present a PCA-based centralized approach and a DPCA-based distributed energy-efficient ...  Read More

QoS-Based web service composition based on genetic algorithm

Mohammad AllamehAmiri; Vali Derhami; Mohammad Ghasemzadeh

Volume 1, Issue 2 , July 2013, , Pages 63-73


  Quality of service (QoS) is an important issue in the design and management of web service composition. QoS in web services consists of various non-functional factors, such as execution cost, execution time, availability, successful execution rate, and security. In recent years, the number of available ...  Read More

On improving APIT algorithm for better localization in WSN

Seyed M. Hosseinirad; M. Niazi; J Pourdeilami; S. K. Basu; A. A. Pouyan

Volume 2, Issue 2 , July 2014, , Pages 97-104


  In Wireless Sensor Networks (WSNs), localization algorithms could be range-based or range-free. The Approximate Point in Triangle (APIT) is a range-free approach. We propose modification of the APIT algorithm and refer as modified-APIT. We select suitable triangles with appropriate distance between anchors ...  Read More

Original/Review Paper
BRTSRDM: Bi-Criteria Regression Test Suite Reduction based on Data Mining

Mohammad Reza Keyvanpour; Zahra Karimi Zandian; Nasrin Mottaghi

Articles in Press, Accepted Manuscript, Available Online from 24 April 2023


  Regression testing reduction is an essential phase in software testing. In this step, the redundant and unnecessary cases are eliminated, whereas software accuracy and performance are not degraded. So far, various researches have been proposed in regression testing reduction field. The main challenge ...  Read More

Original/Review Paper
An Intelligent Fuzzy System for Diabetes Disease Detection using Harris Hawks Optimization

Zahra Asghari Varzaneh; Soodeh Hosseini

Articles in Press, Accepted Manuscript, Available Online from 01 May 2023


  This paper proposed a fuzzy expert system for diagnosing diabetes. In the proposed method, at first, the fuzzy rules are generated based on the Pima Indians Diabetes Database (PIDD) and then the fuzzy membership functions are tuned using the Harris Hawks optimization (HHO). The experimental data set, ...  Read More

Original/Review Paper H.5. Image Processing and Computer Vision
A new scheme for lossless meaningful visual secret sharing by using XOR properties

J. Zarepour; Z. Mehrnahad; A.M. Latif

Articles in Press, Accepted Manuscript, Available Online from 06 May 2023


  In this paper, a novel scheme for lossless meaningful visual secret sharing using XOR properties is presented. In the first step, genetic algorithm with an appropriate proposed objective function created noisy share images. These images do not contain any information about the input secret image and ...  Read More

Original/Review Paper
Improved Facial Action Unit Recognition using Local and Global Face Features

Amin Rahmati; Foad Ghaderi

Articles in Press, Accepted Manuscript, Available Online from 08 May 2023


  Every facial expression involves one or more facial action units appearing on the face. Therefore, action unit recognition is commonly used to enhance facial expression detection performance. It is important to identify subtle changes in face when particular action units occur. In this paper, we propose ...  Read More

Technical Paper
MoGaL: Novel Movie Graph Construction by Applying LDA on Subtitle

Mohammad Nazari; Hossein Rahmani; Dadfar Momeni; Motahare Nasiri

Articles in Press, Accepted Manuscript, Available Online from 08 May 2023


  Graph representation of data can better define relationships among data components and thus provide better and richer analysis. So far, movies have been represented in graphs many times using different features for clustering, genre prediction, and even for use in recommender systems. In constructing ...  Read More

Original/Review Paper
A Deep Learning-based Model for Gender Recognition in Mobile Devices

Fatemeh Alinezhad; Kourosh Kiani; Razieh Rastgoo

Articles in Press, Accepted Manuscript, Available Online from 08 May 2023


  Gender recognition is an attractive research area in recent years. To make a user-friendly application for gender recognition, having an accurate, fast, and lightweight model applicable in a mobile device is necessary. Although successful results have been obtained using the Convolutional Neural Network ...  Read More

Original/Review Paper
An Unsupervised Anomaly Detection Model for Weighted Heterogeneous Graph

Maryam Khazaei; Nosratali Ashrafi-Payaman

Articles in Press, Accepted Manuscript, Available Online from 31 May 2023


  Nowadays, whereas the use of social networks and computer networks is increasing, the amount of associated complex data with graph structure and their applications, such as classification, clustering, link prediction, and recommender systems, has risen significantly. Because of security problems and ...  Read More

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