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Journal of Artificial Intelligence and Data Mining
ISSN 2322-5211 · Shahrood University of Technology · 449 artikel di indeks
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Detection of Driver Distraction Using Spatio-Temporal Graph Convolutional Networks (ST-GCN) and Attention Mechanism
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…
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Balancing and Refining Representations for DTI Prediction: A Framework Combining One-SVM-US and a Modified VAE
Accurate prediction of drug–target interactions is essential for advancing drug discovery and repositioning efforts. This study introduces a comprehensive framework that effectively addresses key challenges in DTI prediction, including dataset imbalance and high-dimensional feature representations…
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Conditional Spatial Gustafson-Kessel Clustering Algorithm Based on Information Theory for Segmenting Brain MRI Images
Magnetic Resonance Imaging (MRI) often suffers from noise and Intensity Non-Uniformity (INU), making segmentation a challenging task. The Fuzzy C-Means (FCM) algorithm, a widely used clustering method for image segmentation, is highly sensitive to noise and its convergence rate depends on data…
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Dynamic Retrieval-Based Prompting for Cross-Lingual Dialogue Understanding in Persian
Dialogue understanding for low-resource languages like Persian remains challenging due to limited annotated data, which constrains supervised training at scale. We propose a simple yet effective training-free method that combines machine translation, retrieval-based example selection, and prompting…
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Improving Ball Detection in Volleyball Using Deep Learning
In recent years, the application of deep learning techniques has revolutionized various domains, including the realm of sports analytics. The analysis of ball tracking and trajectory in sports has become an increasingly vital area of research, driven by advancements in technology and the growing…
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Low-light Image Enhancement Based on Retinex Theory Using the Evolutionary PSO Algorithm
This paper introduces a novel approach to enhance the quality of images captured under low-light conditions. The method optimizes the parameters of the established Li method by employing the evolutionary Particle Swarm Optimization (PSO) algorithm. A key contribution of this research is the…
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Automated Surface Defect Detection in Copper Blanks Using YOLOv8 Segmentation and EfficientNetV2-S Classification
In this study, an intelligent deep learning–based system is proposed for automated detection of surface defects in copper cathode blanks used in the electrorefining process. The proposed pipeline combines a YOLOv8-based segmentation model with an EfficientNetV2-S classifier to localize and analyze…
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Ensemble Learning for Speech Emotion Recognition using Graph-Based Signal Dynamics
Nowadays, the recognition of emotions using speech signals has gained popularity because of its vast number of applications in different fields such as medicine, online marketing, online search engines, education systems, criminal investigations, traffic collisions, and more. Many researchers have…
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GAN-Based Anomaly Detection in Social Networks Text Data Using Lasso and Ridge Regression Models
Identifying and classifying anomalies in textual data from social networks is challenging due to the linguistic complexity and diverse user expressions. While deep learning and machine learning techniques offer promise in tackling this problem, their effectiveness is limited by insufficient data…
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A Novel Hybrid Deep Learning Model with Receptive Field-Enhanced Skip Connections and Adaptive Loss for Medical Image Segmentation
Medical image analysis, crucial for disease diagnosis and treatment, often suffers from the challenge of class imbalance, where the area of normal tissue significantly outweighs that of abnormal regions. Furthermore, the varying class ratios across different images within a dataset complicate the…
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Sign Language Recognition Using a Hybrid Model Based on Convolutional Neural Networks and Hidden Markov Models
In recent years, sign language recognition has emerged as a major challenge in the fields of image processing and machine learning. People with hearing impairments use sign language to communicate, but the lack of automated tools to translate it has created significant communication barriers. This…
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PWNC: A Large-Scale Persian Corpus for Joint WSD and NER Using Semi-Supervised and Supervised Learning
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…
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DeGF Network -ABSA: Hybrid Approach with DeBERTa and Gated Fusion
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…
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A Novel Fault Prediction Technique for Oil-Immersed Transformers Based on Advanced Gradient Boosting and Particle Swarm Optimization (PSO)
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…
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Accuracy Improvement of Collaborative Recommender System Using Deep Learning
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…
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A Hybrid Approach to Stock Market Forecasting with LSTM, Modified Complex Variational Mode Decomposition, and Secretary Bird Optimization Algorithm
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…
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A Hybrid Approach for Brain Tumor Classification: Enhancing MRI-Based Diagnosis with CNN-Transformer Synergy
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…
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User Bias and Algorithmic Accuracy in Football Outcome Prediction: Evidence from the English Premier League
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…
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Comparative Evaluation of Deep Learning Architectures for Printed and Handwritten Farsi OCR
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…
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K-means-CRBM: An Efficient Unsupervised Tool for Feature Learning
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…