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)

Facts & Figures

(Up to Date)

Number of Volumes

12

Number of Articles

421

Number of Issues

34

Article View

685,325

PDF Download

580,734

View Per Article

1627.85

PDF Download Per Article

1379.42

Reject Rate %

56

Acceptance Rate %

26

Number of Reviewers

3283

First Decision

(Approximately)

40(Days)

 

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.3.9. Problem Solving, Control Methods, and Search
Event-Triggered Optimal Adaptive Leader-Follower Consensus Control for Unknown Input-Constrained Discrete-Time Nonlinear Systems

Zahra Jahan; Abbas Dideban; Farzaneh Tatari

Volume 12, Issue 2 , April 2024, Pages 149-161

https://doi.org/10.22044/jadm.2024.13779.2500

Abstract
  This paper introduces an adaptive optimal distributed algorithm based on event-triggered control to solve multi-agent discrete-time zero-sum graphical games for unknown nonlinear constrained-input systems with external disturbances. Based on the value iteration heuristic dynamic programming, the proposed ...  Read More

Original/Review Paper H.3. Artificial Intelligence
An Intelligent Blockchain-Based System Configuration for Screening, Monitoring, and Tracing of Pandemics

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

Volume 12, Issue 2 , April 2024, Pages 163-191

https://doi.org/10.22044/jadm.2024.14379.2541

Abstract
  This study proposes a high-level design and configuration for an intelligent dual (hybrid and private) blockchain-based system. The configuration includes the type of network, level of decentralization, nodes, and roles, block structure information, authority control, and smart contracts and intended ...  Read More

Original/Review Paper H.3. Artificial Intelligence
Selecting Optimal Moments of Chest Images by Partialized-Dual-Hybrid Feature Selection Scheme for Morphological-based COVID-19 Diagnosis

Seyed Alireza Bashiri Mosavi; Mohsen Javaherian; Omid Khalaf Beigi

Volume 12, Issue 2 , April 2024, Pages 193-215

https://doi.org/10.22044/jadm.2024.14359.2540

Abstract
  One way of analyzing COVID-19 is to exploit X-ray and computed tomography (CT) images of the patients' chests. Employing data mining techniques on chest images can provide in significant improvements in the diagnosis of COVID-19. However, in feature space learning of chest images, there exists a large ...  Read More

Applied Article H.5.7. Segmentation
Enhancing Image Segmentation with Darwinian Grey Wolf Optimizer: A Novel Multilevel Thresholding Approach

Ehsan Ehsaeyan

Volume 12, Issue 2 , April 2024, Pages 217-225

https://doi.org/10.22044/jadm.2024.14469.2550

Abstract
  This paper presents a novel approach to image segmentation through multilevel thresholding, leveraging the speed and precision of the technique. The proposed algorithm, based on the Grey Wolf Optimizer (GWO), integrates Darwinian principles to address the common stagnation issue in metaheuristic algorithms, ...  Read More

Original/Review Paper H.3.9. Problem Solving, Control Methods, and Search
Position Tracking Control of ASV based on Dynamic Inversion with Intelligent Methods

Heydar Toossian Shandiz; Mohsen Erfan Hajipour; Amir Ali Bagheri

Volume 12, Issue 2 , April 2024, Pages 227-240

https://doi.org/10.22044/jadm.2024.14081.2516

Abstract
  The aim of this paper is to create an efficient controller that can precisely track the position of autonomous surface vessels by utilizing the dynamic inversion control technique. One of the key objectives of this controller is to mitigate or eliminate the effects of environmental disturbances like ...  Read More

Original/Review Paper H.3.2.2. Computer vision
A Deep Learning-based Model for Fingerprint Verification

Mobina Talebian; Kourosh Kiani; Razieh Rastgoo

Volume 12, Issue 2 , April 2024, Pages 241-248

https://doi.org/10.22044/jadm.2024.14298.2531

Abstract
  Fingerprint verification has emerged as a cornerstone of personal identity authentication. This research introduces a deep learning-based framework for enhancing the accuracy of this critical process. By integrating a pre-trained Inception model with a custom-designed architecture, we propose a model ...  Read More

Original/Review Paper H.3.2.2. Computer vision
Exploring Object Detection Methods for Autonomous Vehicles Perception: A Comparative Study of Classical and Deep Learning Approaches

Zobeir Raisi; Valimohammad Nazarzehi; Rasoul Damani; Esmaeil Sarani

Volume 12, Issue 2 , April 2024, Pages 249-261

https://doi.org/10.22044/jadm.2024.14241.2529

Abstract
  This paper explores the performance of various object detection techniques for autonomous vehicle perception by analyzing classical machine learning and recent deep learning models. We evaluate three classical methods, including PCA, HOG, and HOG alongside different versions of the SVM classifier, and ...  Read More

Applied Article H.5. Image Processing and Computer Vision
BNPL-Dataset: A New Benchmark Dataset for Visual Disease Detection of Barberry, Jujube, and Pomegranate Trees

Jalaluddin Zarei; Mohammad Hossein Khosravi

Volume 12, Issue 2 , April 2024, Pages 263-272

https://doi.org/10.22044/jadm.2024.14480.2552

Abstract
  Agricultural experts try to detect leaf diseases in the shortest possible time. However, limitations such as lack of manpower, poor eyesight, lack of sufficient knowledge, and quarantine restrictions in the transfer of diseases to the laboratory can be acceptable reasons to use digital technology to ...  Read More

Original/Review Paper D. Data
A Novel Combination of Segmentation, Ensemble Clustering and Genetic Algorithm for Clustering Time Series

Zahra Ghorbani; Ali Ghorbanian

Volume 12, Issue 2 , April 2024, Pages 273-286

https://doi.org/10.22044/jadm.2024.14170.2526

Abstract
  Increasing the accuracy of time-series clustering while reducing execution time is a primary challenge in the field of time-series clustering. Researchers have recently applied approaches, such as the development of distance metrics and dimensionality reduction, to address this challenge. However, using ...  Read More

Original/Review Paper H.5. Image Processing and Computer Vision
You Look at the Face of an Angel: An Innovative Hybrid Deep Learning Approach for Detecting Down Syndrome in Children's Faces Through Facial Analysis

Khosro Rezaee

Volume 12, Issue 2 , April 2024, Pages 287-303

https://doi.org/10.22044/jadm.2024.14817.2580

Abstract
  Traditional Down syndrome identification often relies on professionals visually recognizing facial features, a method that can be subjective and inconsistent. This study introduces a hybrid deep learning (DL) model for automatically identifying Down syndrome in children's facial images, utilizing facial ...  Read More

Technical Paper H.5. Image Processing and Computer Vision
VGG19-DeFungi: A Novel Approach for Direct Fungal Infection Detection Using VGG19 and Microscopic Images

Sekine Asadi Amiri; Fatemeh Mohammady

Volume 12, Issue 2 , April 2024, Pages 305-314

https://doi.org/10.22044/jadm.2024.14731.2575

Abstract
  Fungal infections, capable of establishing in various tissues and organs, are responsible for many human diseases that can lead to serious complications. The initial step in diagnosing fungal infections typically involves the examination of microscopic images. Direct microscopic examination using potassium ...  Read More

Other H.6.2. Models
Diagnosis and Classification of Tuberculosis Chest X-ray Images of Children Less Than 15 years at Mbarara Regional Referral Hospital Using Deep Learning

Simon Kawuma; Elias Kumbakumba; Vicent Mabirizi; Deborah Nanjebe; Kenneth Mworozi; Adolf Oyesigye Mukama; Lydia Kyasimire

Volume 12, Issue 2 , April 2024, Pages 315-324

https://doi.org/10.22044/jadm.2024.14270.2530

Abstract
  Tuberculosis (TB) is an underestimated cause of death in children, with only 45% of cases correctly diagnosed and reported. It is estimated that 1.12 million TB cases occurred among newborns, children, and adolescents aged less or equal 14 years. In Uganda, TB prevalence is 8.5% in children and 16.7% ...  Read More

Iranian Vehicle Images Dataset for Object Detection Algorithm
Volume 12, Issue 1 , January 2024, , Pages 127-136

https://doi.org/10.22044/jadm.2024.13858.2501

Abstract
  Providing a dataset with a suitable volume and high accuracy for training deep neural networks is considered to be one of the basic requirements in that a suitable dataset in terms of the number and quality of images and labeling accuracy can have a great impact on the output accuracy of the trained ...  Read More

Credit scoring in banks and financial institutions via data mining techniques: A literature review
Volume 1, Issue 2 , July 2013, , Pages 119-129

https://doi.org/10.22044/jadm.2013.124

Abstract
  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

Multi-Focus Image Fusion in DCT Domain using Variance and Energy of Laplacian and Correlation Coefficient for Visual Sensor Networks
Volume 6, Issue 2 , July 2018, , Pages 233-250

https://doi.org/10.22044/jadm.2017.5169.1624

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

https://doi.org/10.22044/jadm.2013.98

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

https://doi.org/10.22044/jadm.2013.97

Abstract
  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

Original/Review Paper
Transformer-based Generative Chatbot Using Reinforcement Learning

Nura Esfandiari; Kourosh Kiani; Razieh Rastgoo

Articles in Press, Accepted Manuscript, Available Online from 02 November 2024

https://doi.org/10.22044/jadm.2024.14466.2549

Abstract
  A chatbot is a computer program system designed to simulate human-like conversations and interact with users. It is a form of conversational agent that utilizes Natural Language Processing (NLP) and sequential models to understand user input, interpret their intent, and generate appropriate answer. This ...  Read More

Original/Review Paper
Advanced stock price forecasting using a 1D-CNN-GRU-LSTM model

Fatemeh Moodi; Amir Jahangard Rafsanjani; Sajjad Zarifzadeh; Mohammad Ali Zare Chahooki

Articles in Press, Accepted Manuscript, Available Online from 02 November 2024

https://doi.org/10.22044/jadm.2024.14831.2581

Abstract
  This article proposes a novel hybrid network integrating three distinct architectures -CNN, GRU, and LSTM- to predict stock price movements. Here with Combining Feature Extraction and Sequence Learning and Complementary Strengths can Improved Predictive Performance. CNNs can effectively identify short-term ...  Read More

Applied Article
PTRP: Title Generation Based On Transformer Models

Davud Mohammadpur; Mehdi Nazari

Articles in Press, Accepted Manuscript, Available Online from 05 November 2024

https://doi.org/10.22044/jadm.2024.14633.2565

Abstract
  Text summarization has become one of the favorite subjects of researchers due to the rapid growth of contents. In title generation, a key aspect of text summarization, creating a concise and meaningful title is essential as it reflects the article's content, objectives, methodologies, and findings. Thus, ...  Read More

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