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 H.3. Artificial Intelligence
A New Hybrid Method to Detect Risk of Gastric Cancer using Machine Learning Techniques

Ali Zahmatkesh Zakariaee; Hossein Sadr; Mohamad Reza Yamaghani

Volume 11, Issue 4 , November 2023, Pages 505-515


  Machine learning (ML) is a popular tool in healthcare while it can help to analyze large amounts of patient data, such as medical records, predict diseases, and identify early signs of cancer. Gastric cancer starts in the cells lining the stomach and is known as the 5th most common cancer worldwide. ...  Read More

Original/Review Paper H.6.3.2. Feature evaluation and selection
Auto-UFSTool: An Automatic Unsupervised Feature Selection Toolbox for MATLAB

Farhad Abedinzadeh Torghabeh; Yeganeh Modaresnia; Seyyed Abed Hosseini

Volume 11, Issue 4 , November 2023, Pages 517-524


  Various data analysis research has recently become necessary in to find and select relevant features without class labels using Unsupervised Feature Selection (UFS) approaches. Despite the fact that several open-source toolboxes provide feature selection techniques to reduce redundant features, data ...  Read More

Original/Review Paper H.3. Artificial Intelligence
Autoencoder-PCA-based Online Supervised Feature Extraction-Selection Approach

Amir Mehrabinezhad; Mohammad Teshnelab; Arash Sharifi

Volume 11, Issue 4 , November 2023, Pages 525-534


  Due to the growing number of data-driven approaches, especially in artificial intelligence and machine learning, extracting appropriate information from the gathered data with the best performance is a remarkable challenge. The other important aspect of this issue is storage costs. The principal component ...  Read More

Methodologies H.3. Artificial Intelligence
Identification of Influential Nodes in Social Networks based on Profile Analysis

Zeinab Poshtiban; Elham Ghanbari; Mohammadreza Jahangir

Volume 11, Issue 4 , November 2023, Pages 535-545


  Analyzing the influence of people and nodes in social networks has attracted a lot of attention. Social networks gain meaning, despite the groups, associations, and people interested in a specific issue or topic, and people demonstrate their theoretical and practical tendencies in such places. Influential ...  Read More

Technical Paper H.5. Image Processing and Computer Vision
Using Convolutional Neural Network to Enhance Classification Accuracy of Cancerous Lung Masses from CT Scan Images

Mohammad Mahdi Nakhaie; Sasan Karamizadeh; Mohammad Ebrahim Shiri; Kambiz Badie

Volume 11, Issue 4 , November 2023, Pages 547-559


  Lung cancer is a highly serious illness, and detecting cancer cells early significantly enhances patients' chances of recovery. Doctors regularly examine a large number of CT scan images, which can lead to fatigue and errors. Therefore, there is a need to create a tool that can automatically detect and ...  Read More

Original/Review Paper H.3. Artificial Intelligence
LSTM Modeling and Optimization of Rice (Oryza sativa L.) Seedling Growth using Intelligent Chamber

Hamid Ghaffari; Hemmatollah Pirdashti; Mohammad Reza Kangavari; Sjoerd Boersma

Volume 11, Issue 4 , November 2023, Pages 561-571


  An intelligent growth chamber was designed in 2021 to model and optimize rice seedlings' growth. According to this, an experiment was implemented at Sari University of Agricultural Sciences and Natural Resources, Iran, in March, April, and May 2021. The model inputs included radiation, temperature, carbon ...  Read More

Original/Review Paper H.3. Artificial Intelligence
Fast COVID-19 Infection Prediction with In-House Data Using Machine Learning Classification Algorithms: A Case Study of Iran

Ali Rebwar Shabrandi; Ali Rajabzadeh Ghatari; Nader Tavakoli; Mohammad Dehghan Nayeri; Sahar Mirzaei

Volume 11, Issue 4 , November 2023, Pages 573-585


  To mitigate COVID-19’s overwhelming burden, a rapid and efficient early screening scheme for COVID-19 in the first-line is required. Much research has utilized laboratory tests, CT scans, and X-ray data, which are obstacles to agile and real-time screening. In this study, we propose a user-friendly ...  Read More

Original/Review Paper H.3. Artificial Intelligence
Investigating Shallow and Deep Learning Techniques for Emotion Classification in Short Persian Texts

Mahdi Rasouli; Vahid Kiani

Volume 11, Issue 4 , November 2023, Pages 587-598


  The identification of emotions in short texts of low-resource languages poses a significant challenge, requiring specialized frameworks and computational intelligence techniques. This paper presents a comprehensive exploration of shallow and deep learning methods for emotion detection in short Persian ...  Read More

Technical Paper G.3.7. Database Machines
A Multi-layered Hidden Markov Model for Real-Time Fraud Detection in Electronic Financial Transactions

Abdul Aziz Danaa Abukari; Mohammed Daabo Ibrahim; Alhassan Abdul-Barik

Volume 11, Issue 4 , November 2023, Pages 599-608


  Hidden Markov Models (HMMs) are machine learning models that has been applied to a range of real-life applications including intrusion detection, pattern recognition, thermodynamics, statistical mechanics among others. A multi-layered HMMs for real-time fraud detection and prevention whilst reducing ...  Read More

Technical Paper B.3. Communication/Networking and Information Technology
Exploring Impact of Data Noise on IoT Security: a Study using Decision Tree Classification in Intrusion Detection Systems

S. Mojtaba Matinkhah; Roya Morshedi; Akbar Mostafavi

Volume 11, Issue 4 , November 2023, Pages 609-626


  The Internet of Things (IoT) has emerged as a rapidly growing technology that enables seamless connectivity between a wide variety of devices. However, with this increased connectivity comes an increased risk of cyber-attacks. In recent years, the development of intrusion detection systems (IDS) has ...  Read More

Original/Review Paper H.3. Artificial Intelligence
Applying Twin-Hybrid Feature Selection Scheme on Transient Multi-Trajectory Data for Transient Stability Prediction

Seyed Alireza Bashiri Mosavi; Omid Khalaf Beigi

Volume 11, Issue 4 , November 2023, Pages 627-638


  A speedy and accurate transient stability assessment (TSA) is gained by employing efficient machine learning- and statistics-based (MLST) algorithms on transient nonlinear time series space. In the MLST’s world, the feature selection process by forming compacted optimal transient feature space ...  Read More

Methodologies H.6. Pattern Recognition
Parallel Incremental Mining of Regular-Frequent Patterns from WSNs Big Data

Sadegh Rahmani Rahmani-Boldaji; Mehdi Bateni; Mahmood Mortazavi Dehkordi

Volume 11, Issue 4 , November 2023, Pages 639-648


  Efficient regular-frequent pattern mining from sensors-produced data has become a challenge. The large volume of data leads to prolonged runtime, thus delaying vital predictions and decision makings which need an immediate response. So, using big data platforms and parallel algorithms is an appropriate ...  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

Applied Article H.3. Artificial Intelligence
Enhancing Aspect-based Sentiment Analysis with ParsBERT in Persian Language

Farid Ariai; Maryam Tayefeh Mahmoudi; Ali Moeini

Articles in Press, Accepted Manuscript, Available Online from 28 January 2024


  In the era of pervasive internet use and the dominance of social networks, researchers face significant challenges in Persian text mining, including the scarcity of adequate datasets in Persian and the inefficiency of existing language models. This paper specifically tackles these challenges, aiming ...  Read More

Original/Review Paper F.2.11. Applications
Low-order Robust Controller for DC-DC Quadratic Buck Converter: Design and Implementation

Ali Sedehi; Alireza Alfi; Mohammadreza Mirjafari

Articles in Press, Accepted Manuscript, Available Online from 12 February 2024


  This paper addresses a key challenge in designing a suitable controller for DC-DC converters to regulate the output voltage effectively within a limited time frame. In addition to non-minimum phase behavior of such type of converter, a significant issue, namely parametric uncertainty, can further complicate ...  Read More

Original/Review Paper H.5. Image Processing and Computer Vision
Automatic Brain Tumor Detection in Brain MRI Images using Deep Learning Methods

Farima Fakouri; Mohsen Nikpour; Abbas Soleymani Amiri

Articles in Press, Accepted Manuscript, Available Online from 24 February 2024


  Due to the increased mortality caused by brain tumors, accurate and fast diagnosis of brain tumors is necessary to implement the treatment of this disease. In this research, brain tumor classification performed using a network based on ResNet architecture in MRI images. MRI images that available in the ...  Read More

Technical Paper B.3. Communication/Networking and Information Technology
Intrusion Detection for IoT Network Security with Deep learning

Roya Morshedi; S. Mojtaba Matinkhah; Mohammad Taghi Sadeghi

Articles in Press, Accepted Manuscript, Available Online from 04 March 2024


  IoT devices has witnessed a substantial increase due to the growing demand for smart devices. Intrusion Detection Systems (IDS) are critical components for safeguarding IoT networks against cyber threats. This study presents an advanced approach to IoT network intrusion detection, leveraging deep learning ...  Read More

Applied Article H.3. Artificial Intelligence
X-SHAoLIM: Novel Feature Selection Framework for Credit Card Fraud Detection

Sajjad Alizadeh Fard; Hossein Rahmani

Articles in Press, Accepted Manuscript, Available Online from 04 March 2024


  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

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