H.3.8. Natural Language Processing
Hassan Deldar; Mohammad Mehdi Homayounpour
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 ...
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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 of legal statutes to assist in identifying the need for amendments to laws or specific provisions. The general framework of the proposed process consists of two key components.First, the texts of legal clauses or articles are enriched through the generation of enriched data using large language models, which involves producing embedding vectors, thematic classification,and extracting the provisions of each law. Second, a retrieval-augmented text generation (RAG) system is developed with the aid of large language models to determine conflicts or the need for expurgation in the output, utilizing the enriched data, predefined prompts, and the Chain of Thought (CoT) technique.The proposed method was evaluated on two benchmark datasets.On the COLIEE 2025 dataset, our approach outperformed the 2024 winners in legal implication tasks, achieving an F1 score of 0.6521 with minimal prompting. The second evaluation used over 1,000 legal clauses covering abrogation and neutral rules, yielding an impressive F1 score exceeding 73.41%.The findings of the proposed methodology demonstrate that, even with limited expertise in the legal domain, it is possible to identify conflicts and the necessity for refining legal texts to an acceptable degree within a reasonable timeframe for legal experts, leveraging the capabilities of large language models.
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 ...
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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.
H.5. Image Processing and Computer Vision
Mohammad M. AlyanNezhadi; Hesamoddin Pourrostami; Mousa Nazari; Farzan Afshari
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 ...
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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 that is based on deep learning and has three main components: extracting area of interest, aligning images through a CNN regressor, and classifying coins through a CNN classifier. The method is tested on a set of 130 coins images (71 coins from banks and 59 coins from non-banks) and is benchmarked against baseline models employing feature extraction and SVMs. The proposed method outperforms the baseline with 99% accuracy. The results prove that the model works effectively in authenticating the coins, which enables safe transactions in the gold market.
H.3.2.6. Games and infotainment
Mohammadreza Mohammadnejad; Morteza Dorrigiv; Farzin Yaghmaee
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 ...
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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 nature of gameplay. To identify essential characteristics for in-game recommendation datasets, we perform a cross-domain analysis of widely used recommendation datasets, evaluating their structural and distributional properties, including interaction space, matrix shape, sparsity, and Gini-based feature–shape diversity. Building on these insights, we curate DOTA-Draft, a research-ready dataset from raw professional Dota 2 matches, encoding sequential pick/ban states, patch versions, and match outcomes. Using this dataset, we conduct top-k drafting recommendation tasks and provide baseline results with Bayesian Personalized Ranking (BPR) and GRU4Rec. To facilitate adoption, DOTA-Draft is packaged in a RecBole-compatible format. This work establishes principled benchmarks for in-game recommendation, demonstrates the inadequacy of traditional user–item paradigms in dynamic, adversarial environments, and provides a foundation for developing models that account for sequential, multi-agent decision-making.
H.5. Image Processing and Computer Vision
Amirhossein Zare Kordkheili; Amirreza Zare Kordkheili; Sekine Asadi Amiri
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) ...
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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) enhanced attention ensemble of deep learning networks. Initially, image augmentation is applied to increase data diversity. We utilize two deep neural network models, EfficientNet-B1 and ResNet-101, for tumor detection. First, we enhance the performance of these models by integrating the CBAM attention module into their architectures. Then, we ensemble the two networks using a soft voting strategy to achieve higher detection accuracy. The proposed method is evaluated on the three-class Figshare dataset, achieving an accuracy of 99.09% in detecting tumors in MRI images, which outperforms existing methods. This approach leverages the strengths of an ensemble of models, offering a promising solution for improving the accuracy and reliability of brain tumor detection in medical imaging.
G.5. Information Technology and Systems Applications
Naga Subrahmanyeswari Nimmakayala; Krishna Prasad M H M
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 ...
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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 architectures including ResNet, EfficientNet, Mobilenet, Densenet and VGG19. Among these models, ResNet achieved the highest accuracy of 90.2%. For improving performance, a hybrid combination of hand-crafted features (pHash, HOG, GLCM, Hu Moments, SIFT, ORB and LBP) and transfer learning features (EfficientNet, DenseNet, ResNet, VGG19 MobileNet) was considered. Combined, these features were merged into a single feature vector, and were classified using ML algorithms: Logistic Regression, Naive Bayes, KNN, Decision Tree, Random Forest, Gradient Boosting and XGBoost. XGBoost yielded the highest accuracy of 96.2%. Additionally, deep learning models including Multilayer Perceptron (MLP) and Artificial Neural Networks (ANN) were explored, with ANN slightly outperforming MLP, achieving an accuracy of 98.3% compared to 97.5% for MLP. The results highlight the efficacy of combining traditional and deep learning-based features for improved diagnostic accuracy
H.3.2.2. Computer vision
Mohammad Hossein Khosravi
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 ...
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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 architecture with an InceptionV3 backbone. Our model leverages a composite loss function that combines linear regression loss with a monotonic ranking constraint to jointly optimize for score-level accuracy and perceptual consistency. Unlike prior works that rely on handcrafted features or narrow degradation types, our approach generalizes across diverse distortions commonly observed in scanned and photographed documents. Experimental results on the SOC and SmartDoc-QA datasets demonstrate that the proposed model exhibits a strong correlation with OCR accuracy, achieving SROCC values of 0.952 and 0.873, respectively, and outperforming several state-of-the-art DIQA methods.
H.6.5.10. Remote sensing
Mehdi Alizadeh; Parvin Ahmadi; Masoumeh Azimzadeh
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 ...
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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 as complex landscapes, varying resolutions, and noise in remote sensing images. To overcome these limitations, we propose HURA-Net, an advanced deep learning framework that intelligently integrates UNet++, ResUNet, and an attention mechanism into a unified architecture. By hybridizing these models, HURA-Net effectively combines their strengths—such as multi-scale feature extraction (UNet++), residual learning (ResUNet), and focus on salient regions (attention mechanism)—while minimizing their individual weaknesses. To further enhance performance, we introduce a refined loss function that not only improves segmentation precision but also addresses the class imbalance problem, which is common in boundary detection tasks. Extensive experiments on a diverse dataset of high-resolution satellite images from different regions of Iran demonstrate that HURA-Net significantly outperforms existing state-of-the-art models. Specifically, it achieves a recall of 45.85% (a 15.59% improvement over ResUNet) and an F1-score of 42.62% (7.27% higher than ResUNet), setting a new benchmark for accuracy. Moreover, our study highlights the critical role of strategic data augmentation in boosting model generalization, particularly in handling variations in lighting, crop types, and field shapes. The success of HURA-Net underscores the importance of innovative architecture design, optimized loss functions, and robust training strategies in advancing remote sensing image segmentation.
H.3.12. Distributed Artificial Intelligence
Samira Mavaddati
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, ...
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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.
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. ...
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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.
H.3.13. Intelligent Web Services and Semantic Web
Atefeh Niroomand; Seyyed Hamid Ghafouri; Amid Khatibi Bardsiri
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 ...
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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 the ontology based on user interactions and incorporates temporal information into both knowledge representation and clustering processes, enabling adaptive and real-time modeling of evolving user behaviors.The dataset consists of approximately 500 users and 15,000 time-stamped interaction records collected over four months, including demographic attributes (age and gender), IoT device usage patterns, and temporal features such as timestamp and time of day.The recommendation framework combines ontology-enhanced user-based collaborative filtering with dynamic K-means clustering, leveraging both semantic relationships and behavioral similarities to improve recommendation quality. Experimental evaluation is conducted using Precision, Recall, F1-score, Accuracy, MAE, and RMSE metrics. The model achieves improvements ranging from approximately 2% to 52%, with respect to state-of-the-art non-temporal methods and traditional collaborative filtering techniques, respectively.Furthermore, computational complexity analysis indicates that the additional processing cost introduced by dynamic ontology updates and temporal modeling remains manageable, preserving the practical applicability of the proposed framework in resource-constrained IoT environments.
