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پژوهشهای نظری و کاربردی هوش ماشینی
ISSN 2821-2029 · Yazd University · 60 artikel di indeks
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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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Solving Fuzzy Polynomial Equations Systems Using Wu’s Method
In this paper, we introduce a method grounded in Wu’s algorithm for determining real solutions of systems of fuzzy polynomial equations. We initially calculate the r-cuts of a fuzzy polynomial equations system and derive a parametric representation for the system. Wu’s algorithm is subsequently…
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Predicting the nature of fire based on machine learning: Logistic regression is an interpretable algorithm
One of the duties of firefighting organizations is to submit fire and accident reports to judicial authorities, insurance, and other requesting institutions to make decisions and pay damages, therefore, finding the nature of the fire in such a way that non-operational components have the least…
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Compact Neural Architecture Search for Image Classification Using Gravitational Search Algorithm
This paper presents a compact neural architecture search method for image classification using the Gravitational Search Algorithm (GSA). Deep learning, through multi-layer computational models, enables automatic feature extraction from raw data at various levels of abstraction, playing a key role…
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An efficient mechanism to determine a function for load distribution in fog computing based on the use of learning classification systems
In recent years, the Internet of Things is one of the most popular technologies that facilitate new interactions between people and humans to increase the quality of life. With the rapid development of the Internet of Things, the fog computing model is emerging as an attractive solution for data…
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Using Long Short-Term Memory and Convolutional neural networks to Detect Distributed Denial of Service Attacks
Deep learning is an essential tool for detecting distributed denial of service (DDoS) attacks due to its ability to analyze complex network traffic patterns and respond in real-time. However, a comprehensive examination of the opportunities and challenges in this field is necessary, given its…
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A convolutional Neural Networks-based Approach for Vulnerability Classification of Integrated Circuits against Hardware Trojans
The vulnerability of digital integrated circuits against the Hardware Trojans (HT) has increased in recent decades due to the implementation of more complex systems on them. HTs could become a source of errors or apply to steel important information embedded in the implemented circuits. So…
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Multimodal Data Fusion for Depression Detection on Twitter
Depression is one of the most significant and prevalent mental health disorders in today’s world. Early detection of depression is critical, and this study aims to identify depression in individuals using information derived from social media. The use of social media for various purposes has grown…
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Automatic detection of machine-generated texts based on linguistic models and deep learning
Today, with the significant growth of artificial intelligence and its products, many opportunities and threats have emerged. One of the most famous and popular products of artificial intelligence is text generation, also called machine text. In this research, a new method is introduced that…