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Time Series Data Augmentation for Energy Consumption Data Based on Improved TimeGAN
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62
Correlation-guided decoding strategy for low-resource Uyghur scene text recognition
Published 2024-11-01“…Specifically, (1) CGDS employs a hybrid encoding strategy that combines Convolutional Neural Network (CNN) and Transformer. This hybrid encoding effectively leverages the advantages of both methods: On one hand, the convolutional properties and shared weight mechanism of CNN allow for efficient extraction of local features, reducing dependency on large datasets and minimizing errors caused by similar characters. …”
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63
Skin Lesion Classification Through Test Time Augmentation and Explainable Artificial Intelligence
Published 2025-01-01“…Our findings reveal that Test Time Augmentation enhances the balanced multi-class accuracy of CNN models by up to 0.3%, achieving a balanced accuracy rate of 97.58% on the International Skin Imaging Collaboration (ISIC 2019) dataset. …”
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Using machine learning-based models for personality recognition
Published 2021-09-01“…Owing to the fact that various filter sizes in CNN may influence its performance, we decided to combine CNN with AdaBoost, a classical ensemble algorithm, to consider the possibility of using the contribution of various filter lengths and gasp their potential in the final classification via combining various classifiers with respective filter size using AdaBoost. …”
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NeuroSight: A Deep‐Learning Integrated Efficient Approach to Brain Tumor Detection
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Innovative segmentation technique for aerial power lines via amplitude stretching transform
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67
A digital twin framework with MobileNetV2 for damage detection in slab structures
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Multi task opinion enhanced hybrid BERT model for mental health analysis
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69
Archaeological Site Detection: Latest Results from a Deep Learning Based Europe Wide Hillfort Search
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70
Explainable analysis of infrared and visible light image fusion based on deep learning
Published 2025-01-01“…Firstly, a multimodal image fusion model was proposed based on the advantages of convolutional neural networks (CNN) for local context extraction and Transformer global attention mechanism. …”
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71
MSCPNet: A Multi-Scale Convolutional Pooling Network for Maize Disease Classification
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Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition.
Published 2025-01-01“…Finally, interpretability methods revealed that only a CNN trained for both face identification and object categorization relied on face-like features-such as 'eyes'-to classify pareidolia stimuli as faces, mirroring findings in human perception. …”
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Real-Time Quality Monitoring and Anomaly Detection for Vision Sensors in Connected and Autonomous Vehicles
Published 2025-01-01“…Autonomous vehicles rely on sensor data to obtain information of the internal state of the system and the impact of the external environment to achieve self-driving autonomy. …”
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Progressive Self-Prompting Segment Anything Model for Salient Object Detection in Optical Remote Sensing Images
Published 2025-01-01“…Most existing ORSI-SOD methods rely on pre-trained CNN- or Transformer-based backbones to extract features from ORSIs, followed by multi-level feature aggregation. …”
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