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

432

Number of Issues

35

Article View

688,810

PDF Download

585,225

View Per Article

1594.47

PDF Download Per Article

1354.69

Reject Rate %

56

Acceptance Rate %

26

Number of Reviewers

3305

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).

 

Applied Article H.3.8. Natural Language Processing
PTRP: Title Generation Based On Transformer Models

Davud Mohammadpur; Mehdi Nazari

Volume 12, Issue 3 , July 2024, Pages 325-335

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

Technical Paper H.6.5.13. Signal processing
Deep Learning Approach for Robust Voice Activity Detection: Integrating CNN and Self-Attention with Multi-Resolution MFCC

Khadijeh Aghajani

Volume 12, Issue 3 , July 2024, Pages 337-347

https://doi.org/10.22044/jadm.2024.14839.2582

Abstract
  Voice Activity Detection (VAD) plays a vital role in various audio processing applications, such as speech recognition, speech enhancement, telecommunications, satellite phone, and noise reduction. The performance of these systems can be enhanced by utilizing an accurate VAD method. In this paper, multiresolution ...  Read More

Original/Review Paper H.3.8. Natural Language Processing
Transformer-based Generative Chatbot Using Reinforcement Learning

Nura Esfandiari; Kourosh Kiani; Razieh Rastgoo

Volume 12, Issue 3 , July 2024, Pages 349-358

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 H.3. Artificial Intelligence
Anomaly Detection in Dynamic Graph Using Machine Learning Algorithms

Pouria Rabiei; Nosratali Ashrafi-Payaman

Volume 12, Issue 3 , July 2024, Pages 359-367

https://doi.org/10.22044/jadm.2024.14476.2551

Abstract
  Today, the amount of data with graph structure has increased dramatically. Detecting structural anomalies in the graph, such as nodes and edges whose behavior deviates from the expected behavior of the network, is important in real-world applications. Thus, in our research work, we extract the structural ...  Read More

Other J.10.5. Industrial
Unveiling the Landscape of High-Tech Transfer in Industry 5.0: A Text Mining Exploration

Arezoo Zamany; Abbas Khamseh; Sayedjavad Iranbanfard

Volume 12, Issue 3 , July 2024, Pages 369-392

https://doi.org/10.22044/jadm.2024.14580.2558

Abstract
  The international transfer of high technologies plays a pivotal role in the transformation of industries and the transition to Industry 5.0 - a paradigm emphasizing human-centric, sustainable, and resilient industrial development. However, this process faces numerous challenges and complexities, necessitating ...  Read More

Original/Review Paper F.4.18. Time series analysis
Advanced Stock Price Forecasting Using a 1D-CNN-GRU-LSTM Model

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

Volume 12, Issue 3 , July 2024, Pages 393-408

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

Original/Review Paper H.3. Artificial Intelligence
A New Structure for Perceptron in Categorical Data Classification

Fariba Taghinezhad; Mohammad Ghasemzadeh

Volume 12, Issue 3 , July 2024, Pages 409-421

https://doi.org/10.22044/jadm.2024.14981.2594

Abstract
  Artificial neural networks are among the most significant models in machine learning that use numeric inputs. This study presents a new single-layer perceptron model based on categorical inputs. In the proposed model, every quality value in the training dataset receives a trainable weight. Input data ...  Read More

Original/Review Paper H.3. Artificial Intelligence
Designing a Visual Geometry Group-based Triad-Channel Convolutional Neural Network for COVID-19 Prediction

Seyed Alireza Bashiri Mosavi; Omid Khalaf Beigi; Arash Mahjoubifard

Volume 12, Issue 3 , July 2024, Pages 423-434

https://doi.org/10.22044/jadm.2024.15205.2626

Abstract
  Using intelligent approaches in diagnosing the COVID-19 disease based on machine learning algorithms (MLAs), as a joint work, has attracted the attention of pattern recognition and medicine experts. Before applying MLAs to the data extracted from infectious diseases, techniques such as RAT and RT-qPCR ...  Read More

Original/Review Paper I.4. Life and Medical Sciences
PSALR : Parallel Sequence Alignment for long Sequence Read with Hash Model

Nasrin Aghaee-Maybodi; Amin Nezarat; Sima Emadi; Mohammad Reza Ghaffari

Volume 12, Issue 3 , July 2024, Pages 435-454

https://doi.org/10.22044/jadm.2024.14462.2554

Abstract
  Sequence alignment and genome mapping pose significant challenges, primarily focusing on speed and storage space requirements for mapped sequences. With the ever-increasing volume of DNA sequence data, it becomes imperative to develop efficient alignment methods that not only reduce storage demands but ...  Read More

Original/Review Paper Document and Text Processing
A Transformer-Based Approach with Contextual Position Encoding for Robust Persian Text Recognition in the wild

Zobeir Raisi; Vali Mohammad Nazarzehi

Volume 12, Issue 3 , July 2024, Pages 455-464

https://doi.org/10.22044/jadm.2024.14669.2569

Abstract
  The Persian language presents unique challenges for scene text recognition due to its distinctive script. Despite advancements in AI, recognition in non-Latin scripts like Persian still faces difficulties. In this paper, we extend the vanilla transformer architecture to recognize arbitrary shapes of ...  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
Development of a Persian Mobile Sales Chatbot based on LLMs and Transformer

Nura Esfandiari; Kourosh Kiani; Razieh Rastgoo

Articles in Press, Accepted Manuscript, Available Online from 21 December 2024

https://doi.org/10.22044/jadm.2024.15067.2609

Abstract
  Chatbots are computer programs designed to simulate human conversation. Powered by artificial intelligence (AI), these chatbots are increasingly used to provide customer service, particularly by large language models (LLMs). A process known as fine-tuning LLMs is employed to personalize chatbot answers. ...  Read More

Applied Article
Application of machine learning and metaheuristic optimizer algorithm for crash severity prediction in the urban road network

Morteza Mohammadi Zanjireh; farzad morady

Articles in Press, Accepted Manuscript, Available Online from 21 December 2024

https://doi.org/10.22044/jadm.2024.15103.2614

Abstract
  This paper predicts the severity of crashes based on the analysis of multiple variables and using machine learning methods. For this purpose, data related to the years 2012 to 2024 of Tempe city in the state of Arizona USA was used. Features were selected using the metaheuristic method. Then, by using ...  Read More

Applied Article
FinFD-GCN: Using Graph Convolutional Networks for Fraud Detection in Financial Data

Mohamad Mahdi Yadegar; Hossein Rahmani

Articles in Press, Accepted Manuscript, Available Online from 22 December 2024

https://doi.org/10.22044/jadm.2024.14869.2585

Abstract
  In recent years, new technologies have brought new innovations into the financial and commercial world, giving fraudsters many ways to commit fraud and cost companies big time. We can build systems that detect fraudulent patterns and prevent future incidents using advanced technologies. Machine learning ...  Read More

Original/Review Paper
Fuzzy Clustering of Noisy Images Using a Gaussian Kernel and Spatial Information with Automatic Parameter Tuning and C+ Means

Mohsen Erfani Haji Pour

Articles in Press, Accepted Manuscript, Available Online from 22 December 2024

https://doi.org/10.22044/jadm.2024.15043.2606

Abstract
  The segmentation of noisy images remains one of the primary challenges in image processing. Traditional fuzzy clustering algorithms often exhibit poor performance in the presence of high-density noise due to insufficient consideration of spatial features. In this paper, a novel approach is proposed that ...  Read More

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