Original/Review Paper
F.2.7. Optimization
Alireza Omidi Nasab; Sajad Bastami; Rojiar Pir Mohammadiani; Mohammad Bagher Dowlatshahi; Seyedeh Zahra Mousavi
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
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. This fragility poses a major barrier to building secure and trustworthy AI systems. Conventional defenses, including adversarial training and heuristic detection, often struggle to balance robustness, adaptability, and computational cost. To overcome these limitations, we propose a hybrid adaptive defense framework that unifies Ant Colony Optimization (ACO) with Reinforcement Learning (RL). ACO efficiently explores the high-dimensional space of defense hyperparameters to find globally optimal configurations, while RL enables dynamic, context-aware adaptation of defense strategies in real time. The proposed ACO-RL framework was rigorously evaluated across six diverse benchmark datasets spanning multiple data modalities: MNIST and CIFAR-10 (vision), IMDB and AG News (text), and Cora and Reddit-Binary (graph). Experimental results show that ACO-RL consistently enhances robustness against a wide spectrum of adversarial attacks, outperforming several state-of-the-art baselines. These findings highlight a promising pathway toward developing resilient, cross-domain AI systems capable of defending against evolving adversarial threats.
Original/Review Paper
H.3. Artificial Intelligence
Zainab Hasan; Esmaeel Tahanian
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
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 volumes of Call Detail Records (CDRs). This paper proposes a hybrid ensemble model, termed Hybrid AdaBoost-RF, for telecommunication fraud detection. In this model, Random Forest is employed as the base learner within the AdaBoost framework to enhance the model's robustness against noise. Furthermore, the SMOTE technique is utilized to address the class imbalance problem. Additionally, we applied a decision threshold tuned on the training predictions to improve the model's sensitivity in detecting fraudulent behavior. Experimental results demonstrate that the proposed model outperforms existing methods in recent research, achieving a Recall of 0.87 and an F1-Score of 0.86 on the test partition of the evaluated CDR dataset using the adopted experimental protocol achieving a Recall of 0.87 and an F1-Score of 0.86. Moreover, the Area Under the Curve (AUC) for ROC and PR metrics reach 0.9777 and 0.8733, respectively, validating the high efficiency of the proposed model.
Original/Review Paper
H.3. Artificial Intelligence
Omid Khalaf Beigi; Seyed Alireza Bashiri Mosavi
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
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. Although supervised learning–based approaches have shown satisfactory performance in tumor segmentation and localization, they often suffer from high uncertainty errors along tumor boundaries. In this research, a learning framework combining a supervised model with deep reinforcement learning—referred to as DURL-Net—is proposed for segmentation and refinement purposes. Specifically, the framework first employs a U-Net architecture to generate an initial segmentation mask. This initial output and the corresponding MRI are then partitioned into localized patches, which are sequentially processed by a Deep Q-Network (DQN) agent. The DQN agent interacts with the environment by selecting optimal morphological operations (such as dilation and erosion) to refine tumor boundaries and correct uncertainties patch by patch. The dataset used in this study comprises 3,064 T1-Weighted Contrast-Enhanced MRI images, employed for both segmentation and tumor-type classification tasks. Experimental results demonstrate that DURL-Net achieved a Dice Similarity Coefficient (DSC) of 86.73%, a Jaccard Index (IoU) of 78.68%, a Kappa coefficient (Kap) of 85.21%, a Sensitivity of 87.68%, and a Specificity of 96.06%.
Applied Article
H.3.8. Natural Language Processing
Kiana Rezaei Jafari; Omid Mahdi Ebadati E.; Hamza Khastar
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
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 contexts. In addition, class imbalance in sentiment datasets poses a major challenge, often leading to biased models that underperform on minority classes. In this study, we investigate sentiment analysis on Persian e-commerce reviews by comparing classical machine learning models, including Logistic Regression and SVM, with transformer-based models, namely ParsBERT and ParsRoBERTa. To mitigate the impact of class imbalance, we evaluate Focal Loss against the standard Cross-Entropy Loss. Furthermore, we employ Integrated Gradients within an Explainable Artificial Intelligence (XAI) framework to improve model interpretability and analyze feature contributions. Experimental results on the Digikala dataset demonstrate that ParsBERT trained with Focal Loss achieves the best performance, reaching a Balanced Accuracy of 88.64% and an AUC-ROC of 95.91%. The findings highlight the effectiveness of combining imbalance-aware loss functions with transformer-based architectures for improving minority class detection in Persian sentiment analysis.
Original/Review Paper
H.3.8. Natural Language Processing
Niloofar Ranjbar; Hamed Baghbani
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
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 six deterministic query types and 14 operational perturbation labels grouped into four families. We compare BM25, multilingual dense retrieval, character n-gram TF-IDF, and hybrid retrieval, and evaluate top-1 deterministic answer extraction against a field-specific top-5 RAG system using Qwen2-7B-Instruct. Hybrid retrieval achieves 81.50 MRR@10 on clean queries and 67.76 under perturbation; field-specific RAG reaches 84.17% and 72.08% accuracy, respectively. Clustered paired-bootstrap 95% confidence intervals exclude zero for all principal system differences. English-title noise is the most damaging family, whereas query-form and punctuation variation is comparatively well tolerated. A 43-case consistency audit verifies implementation of the rule-based failure categories, and full-output analysis shows that retrieval-coverage errors dominate the difficult English-title family. These results support component-level evaluation of multilingual RAG robustness.
Research Note
H.3. Artificial Intelligence
Vahidreza Afshin; Saiedeh Kabirirad; Seyed Hamid Zahiri
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
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 framework specifically designed for real-time heart disease detection within IoT–Fog–Cloud environments. The framework integrates advanced preprocessing techniques, including Conditional Generative Adversarial Networks (CGANs) for data balancing and a hybrid feature selection pipeline, thereby addressing data imbalance and dimensionality reduction. It also proposes a dual-path diagnostic system that uses a lightweight XGBoost classifier in the fog layer optimized for low latency and fast local inference, and an improved TabTransformer-based model in the cloud layer, which is selectively used for ambiguous samples. Experimental evaluation on heart disease datasets demonstrates the superior performance of the framework, achieving 99.66% accuracy, F1 score of 99.75%, and AUC of 99.96%. Additionally, it improves overall inference latencies, mostly below one millisecond in the fog layer and low latency in cloud layer. This paper provides a scalable, interpretable, and privacy-preserving medical diagnosis framework in distributed healthcare systems.
Original/Review Paper
H.3.8. Natural Language Processing
Fatemeh Moodi; Hassan Majidi
Abstract
Machine translation of low-resource and domain-specific texts remains a challenging problem, particularly in the absence of appropriate evaluation methodologies. In this study, we propose a multi-reference data augmentation framework for low-resource text translation. Two publicly available, pre-trained ...
Read More
Machine translation of low-resource and domain-specific texts remains a challenging problem, particularly in the absence of appropriate evaluation methodologies. In this study, we propose a multi-reference data augmentation framework for low-resource text translation. Two publicly available, pre-trained Transformer-based machine translation models, mBART-50 and M2M100, are employed and fine-tuned on multi-reference, domain-adapted data. A comprehensive set of automatic evaluation metrics—including BLEU, chrF, BERTScore, METEOR, and COMET—is used in both single-reference and multi-reference settings to assess translation quality. In addition, the translations are evaluated by two human evaluators with expertise in Qur’anic studies and Persian linguistics. The experimental results demonstrate substantial improvements, particularly in terms of BLEU and COMET scores, indicating improved translation performance for this low-resource, domain-specific task. The results further suggest that multi-reference training can improve translation quality and that multi-reference evaluation provides a broader assessment of model performance. However, automatic metrics cannot fully capture the theological, cultural, and interpretive fidelity required for Qur’anic translation; therefore, their results are complemented by human expert evaluation.
Original/Review Paper
H.3.2.2. Computer vision
Fatemeh Naghavi; Kourosh Kiani
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
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. Unlike conventional approaches that rely solely on absolute joint coordinates, the proposed method incorporates short- and long-term frame differences as well as spatial variations across joints to construct complementary views, thereby providing a richer spatiotemporal representation. The architecture consists of multiple convolutional blocks with residual connections, followed by global average pooling and a fully connected layer for final classification. Experimental evaluations conducted on two benchmark datasets, NTU RGB+D and ETRI-Activity3D, demonstrate that while the proposed model may achieve slightly lower accuracy compared to some state-of-the-art methods, it offers high inference speed and low computational complexity. These characteristics make it particularly suitable for real-time applications and deployment on resource-constrained devices, especially in elderly home-care environments.
Original/Review Paper
B.3. Communication/Networking and Information Technology
Sajad Bastami; Mohammad Bagher Dowlatshahi; Rojiar Pir Mohammadiani; Seyedeh Zahra Mousavi
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.
Conceptual Paper
H.5. Image Processing and Computer Vision
Farzane Maghsoudi; Mohammad Javad fadaeiEslam; Farzin Yaghmaee
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
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 architectures. However, the limited receptive field in shallow layers makes it difficult to capture long-range spatial dependencies and non-local context. In this paper, the HiSGAN architecture is proposed to address this limitation. It combines deep representations with traditional techniques, such as SVD and Fast Fourier Convolution (FFC), to effectively extract style-related information and establish a global receptive field. Furthermore, we introduce the HiS-Transformer block with an involution operator in the bottleneck of the generator. This proposed block utilizes hybrid-scale self-attention to adaptively preserve the global receptive field and fine-grained information in salient regions while maintaining low computational cost. HiSGAN employs a new loss function based on gradient contrastive learning to improve cross-domain feature alignment. Quantitative and qualitative results on four public datasets demonstrate the superiority of the proposed approach over state-of-the-art methods. Importantly, these performance gains are achieved while reducing the parameter count and accelerating training. The code is available at https://github.com/OliverRensu/SG-Former
Original/Review Paper
H.5. Image Processing and Computer Vision
Zahrasadat Sajjadi; Soheil Hamzebeigi; Mohsen Soryani
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
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 required for accurate brain age estimation. This study proposes a two-dimensional convolutional neural network (2D CNN)-based multi-task framework for estimating brain age from magnetic resonance imaging (MRI) scans in the ADNI dataset. The framework uses VGG-16 and U-Net encoder backbones with separate heads for age regression and image-rotation classification. We evaluated the effects of several pretraining strategies, including self-supervised pretraining using the DINO framework and supervised pretraining through brain-tumor segmentation. The best-performing configuration, consisting of a DINO-pretrained VGG-16 backbone and a multi-task prediction head, achieved a mean absolute error (MAE) of 3.27 years, which is competitive with previously reported methods. The results indicate that combining transfer learning with multi-task learning improved performance relative to the corresponding single-task models, suggesting that this combination supports the learning of richer and more generalizable feature representations.
Original/Review Paper
A.5. I/O and Data Communications
Somayyeh Jafarali Jassbi; Sajjad Daliri
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
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 presents BCOFF (Blockchain‑based Computation Offloading Framework for Fog), a secure and efficient framework that jointly optimizes resource allocation and enables verifiable task offloading. In BCOFF, resource allocation is performed using the Grey Wolf Optimization (GWO) algorithm, while blockchain provides a tamper-resistant execution record. Specifically, the blockchain serves three purposes: (i) recording offloading decisions and cryptographic hashes of task results to support post‑execution auditability, (ii) validating the integrity of returned results by matching them with the on‑chain hash reference, and (iii) coordinating consensus among fog nodes through a lightweight Validator‑Selection Proof‑of‑Stake (VNPoS) mechanism. VNPoS is a simplified adaptation of the Nominated Proof‑of‑Stake (NPoS) model that selects validators using stake‑based nomination with variance‑aware stake normalization. By avoiding computationally intensive cryptographic puzzles, VNPoS significantly reduces consensus overhead and is therefore suitable for resource‑constrained fog environments. Experimental evaluation using the iFogSim simulator with workloads of 800–1500 tasks shows that BCOFF reduces execution time by 15–27%, lowers host‑selection latency by 22–25%, and decreases energy consumption by 5–9% compared with existing approaches. These results demonstrate that integrating GWO‑based scheduling with the VNPoS blockchain mechanism provides a more efficient and verifiable fog-offloading framework.
Original/Review Paper
H.6.5.2. Computer vision
Mahdi Davari; Razieh Rastgoo
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
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 Attention mechanisms to effectively detect distracted driving behaviors. The ST-GCN component captures spatial and temporal dependencies in 3D skeletal motion data, modeling the dynamic body movements of the driver. Following this, a Transformer Encoder is employed to further refine temporal representations by leveraging global attention, allowing the model to understand long-range dependencies and subtle behavioral patterns over time. In addition, an Attention mechanism is applied to emphasize the most informative joints and time frames. To address class imbalance in the dataset, the model uses a focal loss function, which helps focus training on more difficult-to-classify examples. The proposed approach is validated on the 3D skeletal Drive&Act dataset, where it achieves a high accuracy of 97.47%, outperforming existing models, particularly under challenging conditions such as poor lighting and complex driving environments. The system demonstrates strong potential for real-time driver monitoring, offering an intelligent solution to enhance road safety and reduce accident risks through early detection of driver distraction.
Other
C.3. Software Engineering
Mohammad Mahdi Estesnaei; Saeed Araban
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
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 likely to be faulty. However, the effectiveness of SBFL is hindered by coincidental correctness, which occurs when a fault is executed but no failure is detected. Additionally, traditional SBFL methods assign equal weight to all failed tests, despite some failed tests containing more valuable information. This study aims to enhance SBFL performance by employing a fuzzy expert system to address these challenges. Thirteen open-source subject programs were used to evaluate the efficiency of the proposed FSBFL method. Experimental results, assessed using four key metrics, demonstrate that FSBFL outperforms popular spectrum-based fault localization techniques.
)
