Original/Review Paper
H.3. Artificial Intelligence
Sara Mahmoudi Rashid
Abstract
Teleoperation systems are increasingly deployed in critical applications such as robotic surgery, industrial automation, and hazardous environment exploration. However, these systems are highly susceptible to network-induced delays, cyber-attacks, and system uncertainties, which can degrade performance ...
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Teleoperation systems are increasingly deployed in critical applications such as robotic surgery, industrial automation, and hazardous environment exploration. However, these systems are highly susceptible to network-induced delays, cyber-attacks, and system uncertainties, which can degrade performance and compromise safety. This paper proposes a Graph Neural Network (GNN)-based Digital Twin (DT) framework to enhance the cyber-resilience and predictive control of teleoperation systems. The GNN-based anomaly detection mechanism accurately identifies cyber-attacks, such as false data injection (FDI) and denial-of-service (DoS) attacks, with a detection rate of 24.3% and a false alarm rate of only 1.8%, significantly outperforming conventional machine learning methods. Furthermore, the predictive digital twin model, integrated with model predictive control (MPC), effectively compensates for latency and dynamic uncertainties, reducing control errors by 14.12% compared to traditional PID controllers. Simulation results in a robotic teleoperation testbed demonstrate a 24.4% improvement in trajectory tracking accuracy under variable delay conditions, ensuring precise and stable operation.
Original/Review Paper
H.3.2.2. Computer vision
Fatemeh Asadi-Zeydabadi; Ali Afkari-Fahandari; Elham Shabaninia; Hossein Nezamabadi-pour
Abstract
Farsi optical character recognition remains challenging due to the script’s cursive structure, positional glyph variations, and frequent diacritics. This study conducts a comparative evaluation of five foundational deep learning architectures widely used in OCR—two lightweight CRNN based ...
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Farsi optical character recognition remains challenging due to the script’s cursive structure, positional glyph variations, and frequent diacritics. This study conducts a comparative evaluation of five foundational deep learning architectures widely used in OCR—two lightweight CRNN based models aimed at efficient deployment and three Transformer based models designed for advanced contextual modeling—to examine their suitability for the distinct characteristics of Farsi script. Performance was benchmarked on four publicly available datasets: Shotor and IDPL PFOD2 for printed text, and Iranshahr and Sadri for handwritten text, using word level accuracy, parameter count, and computational cost as evaluation criteria. CRNN based models achieved high accuracy on word level datasets—99.42% (Shotor), 97.08% (Iranshahr), 98.86% (Sadri)—while maintaining smaller model sizes and lower computational demands. However, their accuracy dropped to 78.49% on the larger and more diverse line level IDPL PFOD2 dataset. Transformer based models substantially narrowed this performance gap, exhibiting greater robustness to variations in font, style, and layout, with the best model reaching 92.81% on IDPL PFOD2. To the best of our knowledge, this work is among the first comprehensive comparative studies of lightweight CRNN and Transformer based architectures for Farsi OCR, encompassing both printed and handwritten scripts, and establishes a solid performance baseline for future research and deployment strategies.
Original/Review Paper
I.3.7. Engineering
Elahe Moradi
Abstract
Fault prediction in power transformers is pivotal for safeguarding operational reliability and reducing system disruptions. Leveraging dissolved gas analysis (DGA) data, AI‑driven techniques have recently been employed to enhance predictive performance. This paper introduces a novel machine-learning ...
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Fault prediction in power transformers is pivotal for safeguarding operational reliability and reducing system disruptions. Leveraging dissolved gas analysis (DGA) data, AI‑driven techniques have recently been employed to enhance predictive performance. This paper introduces a novel machine-learning framework that integrates Hist Gradient Boosting (HGB) with a metaheuristic Particle Swarm Optimization (PSO) algorithm for hyperparameter tuning, thereby guaranteeing classifier robustness. The proposed method underwent a two‑stage evaluation: first, Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and HGB were benchmarked, revealing HGB as the most effective method; second, PSO was applied to optimize HGB’s hyperparameters, yielding further performance improvements. Experimental results demonstrate that the hybrid HGB‑PSO model achieves an accuracy of 97.85 %, precision of 98.90 %, recall of 97.33 %, and an F1‑score of 98.99 %. All simulations and comparative analyses against state‑of‑the‑art methods were implemented in Python, and confusion‑matrix analysis was employed to assess predictive performance comprehensively. These findings demonstrate that the hybrid HGB‑PSO method achieves superior accuracy and robustness in transformer fault prediction.
Original/Review Paper
H.6. Pattern Recognition
Samira Mavaddati
Abstract
Brain tumors are among the most life-threatening neurological conditions, requiring precise and early diagnosis for effective treatment planning. Traditional deep learning models, such as Convolutional Neural Networks (CNNs) and ResNet-based architectures, have demonstrated promising results in brain ...
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Brain tumors are among the most life-threatening neurological conditions, requiring precise and early diagnosis for effective treatment planning. Traditional deep learning models, such as Convolutional Neural Networks (CNNs) and ResNet-based architectures, have demonstrated promising results in brain tumor classification. However, these models often struggle to capture long-range dependencies within MRI images, which are crucial for accurate classification. To overcome this limitation, we propose a Hybrid CNN-ViT model, combining the strengths of Vision Transformers (ViT) and CNNs to achieve high-precision brain tumor classification. The CNN component effectively extracts local spatial features, while the ViT module captures global contextual relationships within MRI scans. The model is evaluated on a four-class dataset of Glioma, Meningioma, Pituitary tumors, and non-tumor images, achieving an impressive accuracy of 98.37%, surpassing conventional CNN-based methods. By leveraging transfer learning, the approach enhances classification performance while reducing reliance on large-scale labeled datasets. The proposed Hybrid CNN-ViT model offers a scalable, robust, and efficient solution for real-world neuro-oncological diagnostics, significantly improving the accuracy of MRI-based brain tumor detection.
Original/Review Paper
H.8. Document and Text Processing
Rojiar Pir Mohammadiani; Faezeh Hashemnia; Elham Moradizadeh; Soma Solaiman Zadeh
Abstract
Aspect-Based Sentiment Analysis (ABSA) has become a critical tool for extracting fine-grained insights from user opinions. This paper introduces DeGF-ABSA (DeBERTa-Gated Fusion for Aspect-Based Sentiment Analysis), a novel architecture that addresses key limitations in existing approaches by dynamically ...
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Aspect-Based Sentiment Analysis (ABSA) has become a critical tool for extracting fine-grained insights from user opinions. This paper introduces DeGF-ABSA (DeBERTa-Gated Fusion for Aspect-Based Sentiment Analysis), a novel architecture that addresses key limitations in existing approaches by dynamically balancing global contextual features and aspect features. Unlike traditional methods that rigidly combine context and aspect representations—or transformer-based models lacking explicit mechanisms to disentangle aspect-specific signals—DeGF-ABSA leverages DeBERTa’s disentangled attention mechanism, which excels at modeling positional dependencies in technical texts, paired with a gated fusion layer. This layer adaptively weights the contributions of the context features that come from the [CLS] token, and the aspect-specific features come from the mean of aspect tokens. This helps in accurately determining the sentiment in complex sentences. Experiments on SemEval 2014 datasets achieve state-of-the-art results: 86.68% accuracy (84.50% F1) for Laptops and 91.43% accuracy (86.83% F1) for Restaurants.Cross-domain generalization is critical for aspect-based sentiment analysis, as domain-specific aspects and vocabulary vary significantly. Sentiment expressions also differ across domains, such as 'delicious' for food reviews versus 'fast performance' for electronics, requiring adaptable models to capture contextual nuances. Evaluating the DeGF-ABSA model's performance on datasets from domains beyond laptops and restaurants would provide valuable insights into its ability to generalize and its potential for broader applicability.
Original/Review Paper
H.5. Image Processing and Computer Vision
Reza Kharghanian; Zeynab Mohammadpoory
Abstract
The Convolutional Restricted Boltzmann Machine (CRBM) is a generative model that extracts representations from unlabeled data, achieving success in various applications. However, its unsupervised nature may yield suboptimal representations for specific classification tasks. This paper proposes adapting ...
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The Convolutional Restricted Boltzmann Machine (CRBM) is a generative model that extracts representations from unlabeled data, achieving success in various applications. However, its unsupervised nature may yield suboptimal representations for specific classification tasks. This paper proposes adapting k-means clustering to enhance CRBM parameters, aligning features with informative cluster centers. A novel criterion combining generative and soft-K-Means objectives optimizes both cluster centers and CRBM parameters, allowing for continued unsupervised feature learning.Experiments on MNIST, CIFAR10, and three facial expression datasets (JAFFE, KANADE, BU) show that the proposed method enhances the learning process and offers a more informative representation compared to standard and classification CRBM.
Original/Review Paper
H.3.2.3. Decision support
Fateme Ghasemi; Ameneh Khadivar; Leila Moslehi; Fatemeh Abbasi
Abstract
This study investigates the effectiveness of machine learning algorithms, including Neural Networks, Bayesian Networks, Support Vector Machines, and Random Forests, in predicting football match outcomes using data from the English Premier League (2018–2022).By incorporating user-generated probabilities ...
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This study investigates the effectiveness of machine learning algorithms, including Neural Networks, Bayesian Networks, Support Vector Machines, and Random Forests, in predicting football match outcomes using data from the English Premier League (2018–2022).By incorporating user-generated probabilities for home win, away win, and draw alongside conventional features, the models were evaluated under binary and multi-class classification scenarios. The Support Vector Machine achieved the highest accuracy (69%) in the win-loss scenario, while the Neural Network reached 51% in the win-draw-loss scenario. Results indicate that user-derived features enhance predictive performance, though user predictions show a bias toward home teams, especially in uncertain cases. These findings highlight the potential of integrating user perspectives into predictive modeling and underscore the importance of addressing cognitive bias in sports analytics.
Original/Review Paper
H.3.2.2. Computer vision
Maryam Baghi; Kourosh Kiani; Razieh Rastgoo
Abstract
With rapid advancements in information and communication technology, recommender systems have become vital tools across a wide range of online activities and e-commerce processes. Collaborative recommender systems, which utilize user data and contributions to provide suggestions, represent a significant ...
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With rapid advancements in information and communication technology, recommender systems have become vital tools across a wide range of online activities and e-commerce processes. Collaborative recommender systems, which utilize user data and contributions to provide suggestions, represent a significant innovation in this field. In this paper, we conduct an analysis of collaborative recommender systems and evaluate their impact on enhancing the efficiency and accuracy of recommendations. To this end, we propose a deep learning approach using a Graph Convolutional Network (GCN), as a special type of Graph Neural Network (GNN). By assigning weights to edges between nodes, scores are calculated for these edges. The importance of the edges varies based on the number of neighboring nodes and their proximity to the target node. The higher the edge score, the more significant the path. To calculate edge weights, we leverage metrics such as Jaccard similarity, cosine similarity, LHN index, and Salton cosine similarity. This approach improves the identification of relationships between nodes and enhances the accuracy of the recommender system. For implementation, we utilized the well-known MovieLens dataset. Ultimately, users were clustered into 18 clusters, with a large number of nodes within each cluster. By clustering users, we increased the number and diversity of recommendations. This significantly improved the performance of the recommender system, yielding promising results.
Original/Review Paper
H.3.15.3. Evolutionary computing and genetic algorithms
Homa Mehtarizadeh; Najme Mansouri; Behnam Mohammad Hasani Zade; Mohammad Mehdi Hosseini
Abstract
Accurate and reliable stock price prediction is both a formidable and essential task in financial markets, requiring the use of advanced techniques. This paper presents an innovative approach that integrates Long Short-Term Memory (LSTM) networks with Modified Complex Variational Mode Decomposition (MCVMD) ...
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Accurate and reliable stock price prediction is both a formidable and essential task in financial markets, requiring the use of advanced techniques. This paper presents an innovative approach that integrates Long Short-Term Memory (LSTM) networks with Modified Complex Variational Mode Decomposition (MCVMD) for preprocessing and the Secretary Bird Optimization Algorithm (SBOA) for hyperparameter optimization. In the preprocessing phase, MCVMD decomposes stock price time series into intrinsic mode functions, effectively capturing complex patterns and reducing noise. To enhance predictive performance, SBOA optimizes both the hyperparameters of the LSTM network and the decomposition parameters of MCVMD. The proposed methodology is evaluated on datasets from six companies: Ferrari NV (RACE) and Intesa Sanpaolo (ISP) from Italy, Amadeus IT (AMA) and Repsol (REP) from Spain, and Hitachi Ltd (6501) and Chugai Pharmaceutical Co., Ltd. (4519) from Japan. Results show that the LSTM-MCVMD-SBOA model achieves lower error values compared with conventional benchmarks including ARIMA-GARCH, vanilla LSTM, Long Short-Term Memory-Particle Swarm Optimization (LSTM-PSO), and Long Short-Term Memory-Sine Cosine Algorithm (LSTM-SCA). Compared with these alternatives, SBOA was selected because of its superior balance between exploration and exploitation, inspired by secretary bird hunting and evasion behavior, which enables efficient search in complex optimization landscapes. Overall, the proposed model demonstrates significantly improved predictive accuracy over conventional methods, highlighting the efficacy of combining advanced decomposition with nature-inspired optimization for stock market forecasting.
Original/Review Paper
H.3.8. Natural Language Processing
Arash Keshtkar; Saeedeh Sadat Sadidpour; Hossien Shirazi
Abstract
Word Sense Disambiguation (WSD) is a longstanding challenge in natural language processing, particularly in morphologically rich and low-resource languages such as Persian. The inherent ambiguity of Persian named entities exacerbated by domain-specific contexts and limited labeled data complicates both ...
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Word Sense Disambiguation (WSD) is a longstanding challenge in natural language processing, particularly in morphologically rich and low-resource languages such as Persian. The inherent ambiguity of Persian named entities exacerbated by domain-specific contexts and limited labeled data complicates both semantic interpretation and information extraction. In this study, we introduce the PWNC corpus, a large-scale, integrated dataset designed for both Named Entity Recognition (NER) and WSD in Persian. The corpus was automatically constructed through a semi-supervised framework, incorporating contextual similarity measures and clustering algorithms to annotate ambiguous entities across ten semantic categories. Utilizing a semi-supervised framework, the proposed homograph semantic categorization method achieved robust performance, with a precision of 83%, recall of 81%, and an F1-score of 82% across over 305K annotated paragraphs. Detailed error analysis revealed challenges in disambiguating closely related senses and weak entities, which were mitigated through contextual embedding strategies. This work provides the first publicly available dual-task corpus for Persian NER and WSD, offering a scalable solution for disambiguation in low-resource tasks and laying the baseline for future research in Persian semantic processing.
