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A decentralized federated learning method based on dynamic graphs and multi-criteria decision making for mobile networks
Federated learning, as a new approach in distributed machine learning, enables training of artificial intelligence models without the need to transfer raw data. However, the classical federated learning architecture depends on a central server, which is not available in many real-world applications…
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Tumor Segmentation in MRI using a Transformer Encoder and Adaptive Attention Modules
Accurate and automatic segmentation of brain tumors from Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, treatment planning, and disease monitoring. While Convolutional Neural Network (CNN)-based architectures excel at extracting local features, they are limited in comprehending…
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CAR-FMP: Improved Force-Based Motion Planning for Autonomous Vehicles in Urban Environments
Urban autonomous vehicle navigation remains one of the fundamental challenges in intelligent transportation systems, as it requires real-time decision-making in dynamic traffic conditions, continuous interaction with other vehicles, and appropriate responses to unpredictable obstacles. In this…
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Phantom-Driven Hybrid Deep Learning Classifier Systems for Real-Time Decision Making
Learning Classifier Systems are recognized as a hybrid framework that integrates rule-based interpretability with adaptive learning. However, classical LCS models experience performance degradation when handling high-dimensional, multichannel dynamic data, particularly in real-time applications…
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Outdoor fire detection using the improved YOLO Nano version 8 model
The YOLOv8-nano model is designed as a lightweight network for real-time object detection, but it has limitations in detecting small objects such as fire and smoke. In this study, the proposed model improves performance for small objects by extending the feature pyramid to the P2 scale, adding the…
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Intelligent detection of areas requiring cosmetic treatment and simulation of treatment results using Convolutional Neural Networks and Generative Adversarial Networks
Nowadays, the demand for precise skin condition analysis and for predicting cosmetic treatment outcomes prior to intervention has become a critical requirement in dermatology and aesthetic medicine. In this study, we developed an intelligent deep learning–based framework for detecting facial areas…
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Road Lane Detection Using a Lightweight Architecture Based on the CBAM Attention Module
Lane detection is a critical perception task in intelligent vehicle systems, playing a fundamental role in ensuring safe navigation for autonomous driving and advanced driver-assistance systems (ADAS). However, achieving high detection accuracy under challenging conditions—such as shadows, traffic…
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Convolutional Neural Network and Few-Shot Learning for Non-Contact Heat Rate Estimation from Channel State Information in Data-Limited Scenarios
Non-contact heart rate monitoring using Wi-Fi Channel State Information (CSI) has emerged as a promising approach in biomedical sensing, enabling continuous physiological monitoring without physical contact or wearable devices. However, significant inter-subject variability in CSI patterns and the…
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Multidomain Analysis of Visually Evoked Brain Signals for Multiclass Visual Content Classification: An Integrated Framework Combining Temporal, Spectral, and Spatial Features
This study proposes a comprehensive framework for the multidomain analysis and classification of multiclass visual content based on visually evoked electroencephalography (EEG) signals. Addressing the challenges inherent in extracting meaningful features from EEG data, the proposed approach…
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Improving the pitch of emotional speech using an efficient energy function
An important issue in speech processing is how to beneficially add or change the emotion of a speech. Among the important challenges is calculating the exact value of the main features including energy, pitch, and duration. This research shows that energy feature extraction can be promoted…
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A Lightweight Implementation of a Reduced-Depth SqueezeNet CNN on the Versal Platform for Edge Computing Applications
In recent years, deep neural networks have been widely adopted in computer vision applications. However, deploying these networks on portable and edge devices faces serious challenges due to limited computational resources, memory capacity, and power consumption constraints. One effective approach…
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A Hybrid and Low-Parameter Model Based on Transformer and GRU Architectures for Persian Sentiment Analysis
Sentiment analysis, as a subset of text classification, is a vital tool in areas such as information management, data analysis, and performance improvement. The complexity of Persian texts, due to features such as sarcasm, polysemy, and language combinations, poses unique challenges for sentiment…
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Adapting Coopetition Strategy Models from Other Industries to the Platform Ecosystem of the Insurance Industry: A Systematic Review
BACKGROUND AND OBJECTIVES: The insurance industry has recently undergone profound and disruptive changes under the sweeping influence of digital transformation. The emergence of new technologies such as AI, blockchain, etc.… evolving customer expectations and intensifying competitive pressures have…
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An Analysis of the Antecedents and Consequences of Knowledge Absorptive Capacity in the Insurance Industry
BACKGROUND AND OBJECTIVES: Due to the data‑driven and knowledge‑based nature of insurance industry, the development of this industry depends on enhancing its absorptive capacity, i.e., the ability to acquire, analyze, combine, and apply knowledge. Despite the importance of knowledge absorption for…
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Life Insurance Premium Pricing Through Personalized Mortality Table Using the Wang Premium Principle
BACKGROUND AND OBJECTIVES: The conventional approach to life insurance premium pricing relies on standardized mortality tables, which apply uniform survival probabilities to diverse policyholder populations. While this method is computationally straightforward, it overlooks the heterogeneity of…
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Challenges of Iran's Insurance System in Covering Autonomous Vehicle Risks: Limitations and Reform Requirements
Background and Objectives: Autonomous Vehicles (AVs) have emerged as one of the most transformative innovations in the global transportation sector, reshaping traditional concepts of driving, liability, and risk management. While these technologies promise improved safety and efficiency, they also…
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Forecasting Changes in Financial Solvency of Insurance Companies Using Financial Soundness Indicators
BACKGROUND AND OBJECTIVES: The financial solvency and sustainability of insurance companies is widely recognized as a fundamental cornerstone of financial system stability, given the central role insurers play in risk transfer, loss absorption, and long-term capital allocation. Solvent insurance…
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Advanced Analysis of Transportation Data Using Machine Learning: Bill of Lading Insurance Premium Prediction
BACKGROUND AND OBJECTIVES: Intelligent transportation and logistics management are critical components of modern supply chains, directly influencing cost reduction and risk management. With the rapid increase in transportation data, the need for data-driven approaches to financial decision-making…
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Regulation 98 of the Supreme Insurance Council and Additional Coverages of Employer’s Liability Insurance: Ambiguities and Solutions
BACKGROUNDS AND OBJECTIVES: Employer’s liability insurance, which provides coverage for employers against legal liability for bodily injuries or deaths sustained by employees during the course of employment, is one of the most important fields of liability insurance in Iran’s insurance system…
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Risk Identification and Classification in Iranian Pension Funds: Assessment and Control
BACKGROUND AND OBJECTIVES: Pension funds are one of the most critical pillars of a country’s social security system, tasked with ensuring financial protection for retirees and contributing to socio-economic stability. In Iran, the strategic role of pension funds has become increasingly vital due to…
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