Journal of AI and Data Mining

Original/Review Paper F.2.7. Optimization

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

Volume 14, Issue 4 , October 2026, Pages 465-486

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

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

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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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

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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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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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; Saeed Araban

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

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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Research Note

Dual-Path Learning Framework for Real-Time Heart Disease Detection in IoT-Fog-Cloud Environments Using CGAN and Hybrid Feature Selection

vahidreza afshin; Saiedeh Kabirirad; Seyed Hamid Zahiri

Articles in Press, Accepted Manuscript, Available Online from 26 July 2026

https://doi.org/10.22044/jadm.2026.17225.2858

Abstract

Accurate and timely detection of heart disease is a fundamental challenge in clinical diagnostics, exacerbated by the inherent complexities of real-world healthcare datasets, such as class imbalance, high dimensionality and the presence of outliers. To address these issues, we propose a dual-path learning ... Read More

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Original/Review Paper

A Physics-Informed Cost Function to Enhance Velocity Field Estimation in Deep Learning-Based Particle Image Velocimetry

Hamed Modanloujouybari; Yasser Baleghi

Articles in Press, Accepted Manuscript, Available Online from 26 July 2026

https://doi.org/10.22044/jadm.2026.17374.2876

Abstract

Accurate estimation of velocity fields from Particle Image Velocimetry (PIV) data is essential for fluid-flow analysis and modeling. PIV relies on Image Processing techniques such as cross-correlation and optical flow to estimate the magnitude and direction of fluid motion; however, traditional approaches ... Read More

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Original/Review Paper

Graph-Transformer Reinforcement Learning for Scalable Multi-Agent Coordination

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

Articles in Press, Accepted Manuscript, Available Online from 26 July 2026

https://doi.org/10.22044/jadm.2026.17636.2918

Abstract

Multi-agent reinforcement learning (MARL) is a key paradigm for coordination in robotics, autonomous systems, and distributed control. However, existing MARL methods face fundamental limitations in scalability, adaptability to dynamic environments, and stability under evolving interactions. To address ... Read More Multi-agent reinforcement learning (MARL) is a key paradigm for coordination in robotics, autonomous systems, and distributed control. However, existing MARL methods face fundamental limitations in scalability, adaptability to dynamic environments, and stability under evolving interactions. To address these challenges, we propose Adaptive Graph-Transformer Reinforcement Learning (AGTRL), a framework integrating graph-based relational modelling with transformer attention for adaptive coordination in large-scale multi-agent systems. AGTRL unifies graph-based perception and attention-based coordination in an end-to-end pipeline, encoding role information and adaptively weighting interactions by context. This combination, missing in prior MARL methods, bridges scalability and robustness in dynamic environments. AGTRL constructs a dynamic graph of agent relationships and uses multi-head self-attention to prioritize relevant interactions in real-time, ensuring robust performance under perturbations. We evaluate robustness under communication dropout (up to 40% link removal) and dynamic edge removal, measuring performance via episode reward and win rate. The framework incorporates an adaptive stability-performance trade-off mechanism that maintains learning efficacy in the presence of communication constraints and environmental uncertainty. We introduce a graph-enhanced policy architecture that jointly optimizes individual agent policies and inter-agent coordination through attention-weighted message passing. Comprehensive evaluations on benchmark environments—including StarCraft II micromanagement scenarios, cooperative navigation (Spread), and adversarial tasks (Predator-Prey)—demonstrate that AGTRL achieves superior sample efficiency, scalability, and robustness compared to state-of-the-art MARL baselines. Experimental results show AGTRL improves convergence speed by 32% on average and maintains stable performance with up to 40% communication dropout, establishing its viability for real-world deployment in dynamic multi-agent domains.

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Original/Review Paper

Enhancing CutMix with Grad-CAM: Adaptive Data Augmentation for Improved Deep Learning Models

Matin Gholami; Seyyed Ali Zendehbad; Jamal Ghasemi

Articles in Press, Accepted Manuscript, Available Online from 16 August 2026

https://doi.org/10.22044/jadm.2026.17320.2868

Abstract

Deep neural networks are data-hungry and prone to overfitting, especially with limited training data and fine-grained visual variations. While region-mixing augmentations like CutMix serve as effective regularizers, their purely stochastic nature often creates a semantic gap—critical discriminative ... Read More Deep neural networks are data-hungry and prone to overfitting, especially with limited training data and fine-grained visual variations. While region-mixing augmentations like CutMix serve as effective regularizers, their purely stochastic nature often creates a semantic gap—critical discriminative features are inadvertently masked or replaced, yielding suboptimal training signals. To address this limitation, the authors propose a novel family of adaptive augmentation strategies guided by Grad-CAM attention maps, injecting semantic awareness into the mixing procedure. The framework comprises three variants: Feature-Based CutMix and Saliency-Guided CutMix, which target salient regions for replacement (single peak and multiple high-saliency regions via fixed thresholding, respectively); and Region-Adaptive CutMix (RACutMix), which preserves salient regions by generating non-rectangular, saliency-informed masks using an adaptive percentile-based thresholding mechanism. RACutMix strategically augments only background or low-saliency regions with donor content, producing semantically coherent training examples that maintain visual and label consistency. Systematic evaluation on the Oxford-IIIT Pet benchmark with ResNet-50 compares five training paradigms under identical protocols with repeated trials and significance testing. RACutMix achieves 89.76% test accuracy, a substantial 10.23 percentage point improvement over standard CutMix (79.53%). It also demonstrates faster convergence, more stable loss dynamics, and consistent gains across precision, recall, and F1-score. Training overhead increases by approximately 1.5× time and 0.5 GB peak memory—a cost the authors quantify fully. This work bridges explainable AI and training regularization, demonstrating that internal model attention can be effectively recycled to guide learning toward more robust and discriminative feature representations, validating interpretability-guided augmentation as a powerful paradigm for fine-grained visual recognition tasks.

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Technical Paper

Anomaly Detection for Small, High-Dimensional Healthcare Data

Mahboobeh Riahi-Madvar; zahra Moterassed

Articles in Press, Accepted Manuscript, Available Online from 16 August 2026

https://doi.org/10.22044/jadm.2026.17470.2887

Abstract

Anomaly detection in healthcare datasets is vital for identifying unusual and potentially critical patterns that can influence clinical decisions. However, because many medical datasets are small and have high dimensionality, this task is still a challenge. While deep neural networks have achieved significant ... Read More

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Original/Review Paper

Biological Brain Age Estimation Using Multi-Task Self Supervision and Pre-Trained Deep Neural Networks

Zahrasadat Sajjadi; Soheil Hamzebeigi; Mohsen Soryani

Articles in Press, Accepted Manuscript, Available Online from 16 August 2026

https://doi.org/10.22044/jadm.2026.17592.2904

Abstract

As the global population ages, reliable methods for assessing brain health and age-related changes are increasingly important. Brain age is a promising biomarker of brain health, and machine-learning methods have enabled its estimation from neuroimaging data. However, effective training strategies are ... Read More

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Original/Review Paper

A Hybrid AdaBoost-Random Forest Framework for Telecom Fraud Detection using CDRs

Zainab Hasan; Esmaeel Tahanian

Articles in Press, Accepted Manuscript, Available Online from 16 August 2026

https://doi.org/10.22044/jadm.2026.17863.2956

Abstract

Today, telecommunications fraud has emerged as a major challenge for operators, resulting in billions of dollars in financial losses annually. the presence of substantial noise and severe class imbalance between legitimate and fraudulent data complicates the identification of fraud patterns within massive ... Read More

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Applied Article

Enhancing Minority Class Detection in Persian e-Commerce Sentiment Analysis via Focal Loss and Explainable AI

Kiana Rezaei Jafari; Omid Mahdi Ebadati E.; Hamza Khastar

Articles in Press, Accepted Manuscript, Available Online from 16 August 2026

https://doi.org/10.22044/jadm.2026.17616.2911

Abstract

The rapid growth of e-commerce has led to an increasing volume of Persian user reviews containing valuable opinions about products and services. Sentiment analysis enables automatic extraction of sentiment polarity from such data; however, Persian remains underexplored, especially in real-world e-commerce ... Read More

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Original/Review Paper

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

Mahdi Davari; Razieh Rastgoo

Articles in Press, Accepted Manuscript, Available Online from 25 August 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

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Facts & Figures (Up to Date)
Number of Volumes	14
Number of Articles	534
Number of Issues	43
Article View	962,010
PDF Download	801,505
View Per Article	1801.52
PDF Download Per Article	1500.95
Reject Rate %	53
Acceptance Rate %	27
Number of Reviewers	3437
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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