Journal of AI and Data Mining

Original/Review Paper H.6.5.2. Computer vision

Detection of Driver Distraction Using Spatio-Temporal Graph Convolutional Networks (ST-GCN) and Attention Mechanism

Mahdi Davari; Razieh Rastgoo

Volume 14, Issue 3 , July 2026

https://doi.org/10.22044/jadm.2025.16157.2735

Abstract

Detecting driver distraction is critically important, as it remains a major contributor to road accidents and traffic-related injuries worldwide. This study introduces a novel hybrid deep learning model that integrates Spatio-Temporal Graph Convolutional Networks (ST-GCN) with a Transformer Encoder and ... 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

Skeleton-Based Sign Language Generation Using a Transformer-based Generative Model

Rozhin Mohammadizand; Razieh Rastgoo

Articles in Press, Accepted Manuscript, Available Online from 10 February 2026

https://doi.org/10.22044/jadm.2025.16369.2759

Abstract

Sign language is a structured, non-vocal form of communication primarily used by individuals who are deaf or hard of hearing, who often face challenges interacting with non-signers. To address this, translation systems between sign and spoken language are essential, encompassing sign language recognition ... Read More

Applied Article

Categorizing Rules from the Expurgation Point of View using Large Language Models.

Hassan Deldar; Mohammad Mehdi Homayounpour

Articles in Press, Accepted Manuscript, Available Online from 06 June 2026

https://doi.org/10.22044/jadm.2026.16400.2763

Abstract

In most of the countries, the legislative process has a long history, which has led to increasing diversity and multiplicity of laws. This has made it difficult to access laws that are valid in both time and place. The focus of this article is on the application of artificial intelligence in the domain ... Read More

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Original/Review Paper

BCOFF: A Blockchain-Based Framework with Consensus Protocol to Enhance Efficiency and Ensure Integrity in Fog Computing Offloading

Somayyeh Jafarali Jassbi; Sajjad Daliri

Articles in Press, Accepted Manuscript, Available Online from 06 June 2026

https://doi.org/10.22044/jadm.2026.16548.2777

Abstract

The rapid growth of the Internet‑of‑Things (IoT) imposes significant challenges on task offloading in fog environments, including service latency, resource constraints, and trust management. Fog computing mitigates these limitations by moving computation and storage closer to end devices. This paper ... Read More

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Original/Review Paper

A Deep Learning Approach for Authentication of Original and Non-Original Bank-Issued Gold Coins with Non-Uniform Directions in the Financial Market

Mohammad M. AlyanNezhadi; Hesamoddin Pourrostami; Mousa Nazari; Farzan Afshari

Articles in Press, Accepted Manuscript, Available Online from 06 June 2026

https://doi.org/10.22044/jadm.2025.16177.2738

Abstract

In Iran’s financial market, the authentication of gold coins is majorly required for transparency, reducing fraud, and proper valuation. Differentiating between bank-issued and non-bank-issued coins pose a challenge as their appearance is almost the same. This paper suggests a classification method ... Read More

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Technical Paper

DOTA-Draft: A Dataset for In-Game Recommendation in Multiplayer Online Battle Arenas

Mohammadreza Mohammadnejad; Morteza Dorrigiv; Farzin Yaghmaee

Articles in Press, Accepted Manuscript, Available Online from 06 June 2026

https://doi.org/10.22044/jadm.2026.16888.2819

Abstract

Research in recommender systems has largely relied on standardized datasets such as MovieLens, Amazon Reviews, and Last.fm. However, these datasets are unsuitable for in-game recommendations, particularly in Multiplayer Online Battle Arenas (MOBAs), due to the sequential, team-based, and adversarial ... Read More

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Original/Review Paper

Ensemble of EfficientNet B1 and ResNet 101 with Attention Mechanism for Brain Tumor Classification in MRI Images

Amirhossein Zare Kordkheili; Amirreza Zare Kordkheili; Sekine Asadi Amiri

Articles in Press, Accepted Manuscript, Available Online from 06 June 2026

https://doi.org/10.22044/jadm.2026.17084.2845

Abstract

Brain tumor detection is a critical task in medical imaging, requiring accurate and reliable methods. Recent advancements in deep learning have shown great potential in this field. In this article, we present a novel method for brain tumor detection based on a Convolutional Block Attention Module (CBAM) ... Read More

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Technical Paper

Enhanced Breast Cancer Detection using Hybrid Feature Extraction through Machine Learning and Deep Learning Techniques

Naga Subrahmanyeswari Nimmakayala; Krishna Prasad M H M

Articles in Press, Accepted Manuscript, Available Online from 06 June 2026

https://doi.org/10.22044/jadm.2026.17095.2846

Abstract

Breast cancer detection is critical for early diagnosis and treatment. This paper utilized the BreakHis dataset, comprising 7,907 histopathological images of breast tumors (benign and malignant) captured at varying magnification levels. Initially, a basic CNN was applied, followed by advanced deep learning ... Read More

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Original/Review Paper

A Siamese Network Based on InceptionV3 with Custom Loss Functions for Document Image Quality Assessment (DIQA)

Mohammad Hossein Khosravi

Articles in Press, Accepted Manuscript, Available Online from 07 June 2026

https://doi.org/10.22044/jadm.2026.16621.2788

Abstract

Document Image Quality Assessment (DIQA) is critical for ensuring the reliability of downstream applications such as Optical Character Recognition (OCR), digital archiving, and automated document workflows. In this paper, we propose a deep learning-based DIQA framework using a Siamese neural network ... Read More

Original/Review Paper

HURA-Net: A New Model for Agricultural Field Boundary Detection

Mehdi Alizadeh; Parvin Ahmadi; Masoumeh Azimzadeh

Articles in Press, Accepted Manuscript, Available Online from 28 June 2026

https://doi.org/10.22044/jadm.2026.16909.2823

Abstract

Field boundary detection is a critical task in modern agriculture, enabling precision farming, optimized resource management, and efficient crop monitoring. Despite its importance, existing deep learning models often fail to achieve high accuracy in delineating field boundaries due to challenges such ... Read More

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Original/Review Paper

Context-Aware Criminal Activity Recognition in Surveillance Images Using an Attention-Guided YOLOv10-Vision Transformer Framework

Samira Mavaddati

Articles in Press, Accepted Manuscript, Available Online from 01 July 2026

https://doi.org/10.22044/jadm.2026.17656.2925

Abstract

The rapid growth of intelligent surveillance systems has increased the demand for accurate and efficient criminal activity recognition methods capable of operating in real-world environments. Although conventional deep learning and object detection frameworks have demonstrated promising performance, ... Read More The rapid growth of intelligent surveillance systems has increased the demand for accurate and efficient criminal activity recognition methods capable of operating in real-world environments. Although conventional deep learning and object detection frameworks have demonstrated promising performance, they often struggle to capture long-range contextual dependencies and complex interactions present in surveillance scenes. To address these limitations, this study proposes a hybrid deep learning framework that combines the real-time detection capability of YOLOv10 with the global contextual modeling power of Vision Transformers (ViT). An attention-guided feature fusion mechanism is introduced to effectively integrate local spatial representations extracted by YOLOv10 with global semantic features generated by the transformer architecture. The proposed framework is evaluated on the UCF-Crime dataset, which consists of fourteen categories of normal and criminal activities, including burglary, robbery, assault, vandalism, shoplifting, and abuse. Surveillance videos are converted into image sequences and analyzed under two experimental scenarios: (I) a standalone YOLOv10 model and (II) the proposed Attention-Guided YOLOv10-ViT framework. Performance is assessed using accuracy, precision, recall, and F1-score metrics. Experimental results show that the standalone YOLOv10 model achieves an overall classification accuracy of 88.07%, outperforming the previously reported YOLOv8 baseline. More importantly, the proposed hybrid framework attains an accuracy of 93.45%, exceeding both YOLOv10 and earlier YOLOv8-ViT architectures. The improvement is particularly evident in challenging scenarios involving occlusion, illumination changes, cluttered backgrounds, and crowded environments. The results demonstrate that integrating YOLOv10, Transformers, and attention-guided feature fusion provides a scalable, robust, and real-time solution for intelligent surveillance and public monitoring applications.

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Original/Review Paper

Skeleton based Human Action Recognition for Monitoring Elderly People

Fatemeh Naghavi; Kourosh Kiani

Articles in Press, Accepted Manuscript, Available Online from 04 July 2026

https://doi.org/10.22044/jadm.2026.16786.2810

Abstract

Monitoring the daily activities of elderly individuals plays a crucial role in accident prevention, health assessment, and improving quality of life. In this paper, we propose a lightweight and efficient convolutional neural network architecture for human activity recognition based on skeletal data. ... Read More

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Methodologies

A Time-Aware Internet of Things Recommender System Based on Dynamic Ontologies

Atefeh Niroomand; Seyyed Hamid Ghafouri; Amid Khatibi Bardsiri

Articles in Press, Accepted Manuscript, Available Online from 14 July 2026

https://doi.org/10.22044/jadm.2026.17046.2843

Abstract

This study addresses the challenges of managing dynamic and heterogeneous Internet of Things (IoT) data by proposing a time-aware recommender system that integrates a dynamic semantic ontology with clustering techniques and a hybrid collaborative filtering framework. The proposed model continuously updates ... Read More

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Original/Review Paper

Predicting Hydrogen Combustion Characteristics Under Nitrogen Dilution: A Comparative Evaluation of GPR, MLP, and DNN Models

Ali Asadi; Morteza Noferesti

Articles in Press, Accepted Manuscript, Available Online from 18 July 2026

https://doi.org/10.22044/jadm.2026.16390.2761

Abstract

Hydrogen combustion has emerged as a pivotal technology for decarbonizing the energy sector, offering a clean and sustainable alternative to fossil fuels. This study investigates hydrogen combustion dynamics in a perfectly stirred reactor (PSR) under steady-state, non-premixed conditions. It is employing ... Read More

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Original/Review Paper

A Stacked Ensemble and Deep Latent Embedding Architecture for Robust Restaurant Rating Prediction

mohammad khaki; fereshte dehghani

Articles in Press, Accepted Manuscript, Available Online from 18 July 2026

https://doi.org/10.22044/jadm.2026.16679.2795

Abstract

The exponential proliferation of online food platforms has fundamentally transformed restaurant discovery; however, traditional recommendation systems frequently struggle with the "cold-start" problem and an inability to capture complex, non-linear synergies between diverse restaurant attributes. To ... Read More

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Research Note

A Note on “Super-resolution Reconstruction of Brain Magnetic Resonance Images via Lightweight Autoencoder”

Mohammad Heydari

Articles in Press, Accepted Manuscript, Available Online from 18 July 2026

https://doi.org/10.22044/jadm.2026.17509.2888

Abstract

Deep learning–based super-resolution has become an important tool for enhancing brain magnetic resonance imaging (MRI), particularly when acquisition constraints limit spatial resolution. Lightweight autoencoder architectures have recently been proposed to achieve computational efficiency while ... Read More

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Methodologies

Automatic Digital Modulation Classification Using STFT Spectrograms, Residual Networks, and PSO-Based Hyperparameter Optimization

mansoor zeinali; Mohammad Ahmad Hadi

Articles in Press, Accepted Manuscript, Available Online from 18 July 2026

https://doi.org/10.22044/jadm.2026.16887.2822

Abstract

This paper proposes an automatic modulation classification (AMC) framework that combines STFT spectrograms, a custom four-block ResNet, and Particle Swarm Optimization (PSO) for hyperparameter tuning. Its main contribution is the end-to-end integration of meta-heuristic optimization, systematic ablation ... Read More

Original/Review Paper

DURL-Net: Integrating EfficientNet-B7 U-Net and Deep Q-Network for Brain Tumor Segmentation and Adaptive Morphological Refinement

Omid Khalaf Beigi; Seyed Alireza Bashiri Mosavi

Articles in Press, Accepted Manuscript, Available Online from 18 July 2026

https://doi.org/10.22044/jadm.2026.17571.2896

Abstract

A brain tumor is one of the most serious and life-threatening brain diseases that can profoundly affect an individual’s life. Accordingly, the present study addresses the challenge of refining brain tumor segmentation based on Magnetic Resonance Imaging (MRI) data and deep reinforcement learning. ... Read More

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Applied Article

Named Entity Recognition from Official Texts Based on Multi-Agent Architecture in Large Language Models

Mohammad Aalishahi; Mohammad Hadi Bokaei; Abolfazl Nadi

Articles in Press, Accepted Manuscript, Available Online from 18 July 2026

https://doi.org/10.22044/jadm.2026.16663.2804

Abstract

Given the importance of Named Entity Recognition (NER), numerous studies have been conducted in this field. However, most research has focused on languages such as English, French, and Arabic. In contrast, studies on Persian remain limited, despite Persian being one of the most widely spoken languages ... Read More

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Conceptual Paper

HiSGAN: Image-to-Image Translation with Involution based Hybrid-Scale Transformer and Contrastive learning

farzane maghsoudi; Mohammad Javad fadaeiEslam; Farzin Yaghmaee

Articles in Press, Accepted Manuscript, Available Online from 18 July 2026

https://doi.org/10.22044/jadm.2026.16833.2815

Abstract

Image-to-image translation is a highly challenging task, as it requires an accurate understanding of image details and their consistent transformation across domains. Notably, GANs have achieved remarkable success in this field. In essence, convolutional layers are the primary building blocks of these ... Read More

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Other

FSBFL: A fuzzy expert system for improving spectrum-based fault localization

Mohammad Mahdi Estesnaei

Articles in Press, Accepted Manuscript, Available Online from 18 July 2026

https://doi.org/10.22044/jadm.2026.17502.2890

Abstract

Spectrum-based fault localization (SBFL) is a widely used technique that utilizes coverage data and test outcomes to calculate a suspiciousness score for each program statement. The fundamental hypothesis of SBFL is that a statement covered by more failed test cases and fewer passed test cases is more ... Read More

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Original/Review Paper

Robust Multilingual RAG under Query Perturbations: An English-Persian Benchmark

Niloofar Ranjbar; Hamed Baghbani

Articles in Press, Accepted Manuscript, Available Online from 18 July 2026

https://doi.org/10.22044/jadm.2026.17608.2908

Abstract

Retrieval-augmented generation (RAG) is commonly evaluated on clean inputs that underrepresent realistic multilingual variation. We present an English-Persian movie-domain robustness benchmark built from a corpus of 31,564 records, 120 clean queries, and 720 aligned perturbations. The benchmark covers ... Read More

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Original/Review Paper

A Hybrid Ant Colony Optimization and Reinforcement Learning Framework for Enhancing Neural Network Robustness against Adversarial Attacks

alireza Omidi nasab; Sajad Bastami; Rojiar Pir Mohammadiani; Mohammad Bagher Dowlatshahi; Seyedeh Zahra Mousavi

Articles in Press, Accepted Manuscript, Available Online from 18 July 2026

https://doi.org/10.22044/jadm.2026.17635.2917

Abstract

Deep Neural Networks (DNNs) are increasingly deployed in safety-critical domains such as autonomous driving, healthcare, finance, and natural language processing, yet they remain vulnerable to adversarial attacks—subtle manipulations that can cause confident misclassifications or misleading predictions. ... Read More

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Original/Review Paper

GTGAN: Transformer-based implicit latent GAN with Guidance classifiers for conditional text generation

omid hajipoor; Ahmad Nickabadi; Mohammad Mehdi Homayounpour

Articles in Press, Accepted Manuscript, Available Online from 18 July 2026

https://doi.org/10.22044/jadm.2026.17699.2930

Abstract

Conditional text generation is crucial in natural language processing but often struggles with the high computational costs of Large Language Models (LLMs) and training instability in Generative Adversarial Networks (GANs). In this paper, we introduce the Guidance-Transformer Generative Adversarial Network ... Read More

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Original/Review Paper

Reconstructed Phase Space-Based Deep Learning Framework for Detecting Cardiac Abnormalities from PCG Signals

AGHIL Kashir Taghartapeh; Nader Javadifar; Ali Harimi; Seyed Mehdi BagheriMofidi; Aziz Kalteh

Articles in Press, Accepted Manuscript, Available Online from 18 July 2026

https://doi.org/10.22044/jadm.2026.17807.2948

Abstract

This study investigates the effectiveness of integrating nonlinear dynamical representations derived from reconstructed phase space (RPS) analysis with deep convolutional neural networks for phonocardiogram classification. It evaluates how the nonlinear dynamic information present in cardiac signals ... Read More

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Facts & Figures (Up to Date)
Number of Volumes	14
Number of Articles	511
Number of Issues	43
Article View	935,126
PDF Download	778,947
View Per Article	1829.99
PDF Download Per Article	1524.36
Reject Rate %	53
Acceptance Rate %	27
Number of Reviewers	3412
First Decision (Approximately)	40(Days)

Volume 14 (2026)

Volume 13 (2025)

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)

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