Automatic segmentation of the bronchial tree from CT imaging is important, as it provides structural information for disease diagnosis. Despite the merits of previous automatic bronchus segmentation methods, they have...
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In recent years, social media has become one of the most popular ways of news dissemination. There is a phenomenon that numerous fake news are spreading rampantly on public social media platforms, posing a serious thr...
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ISBN:
(数字)9798331516024
ISBN:
(纸本)9798331516031
In recent years, social media has become one of the most popular ways of news dissemination. There is a phenomenon that numerous fake news are spreading rampantly on public social media platforms, posing a serious threat to the credibility of social media. Moreover, more and more social media news posts carry multimodal contexts, i.e., utilize not only text but also abundant images to describe the news. However, existing methods only involve the first image along with text in multimodal fake news detection. It severely hampers the extraction of global image semantic information and consequently damages the effectiveness of multimodal fake news detection. To address this issue, we propose a Cross-Image Semantic Fusion based multi-modal fake news detection method (CISF for short). The method uses an adaptive attention diffusion module to model semantic correlations among different images, fully leveraging the contextual dependencies between different images to achieve semantic interaction and fusion among images. On the basis, a global image semantic representation is generated to represent the entire image modality. Finally, the fake news detection is performed based on the multimodal fusion of the text and the global image semantic representation. We conduct experiments on two real-world datasets and demonstrate the effectiveness of the proposed method.
In computer-aided diagnosis, breast cancer classification accuracy of artificial intelligence (AI) method might be influenced by imbalanced classification samples. Model training efficiency also decreases with the inc...
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The application of data augmentation for deep learning (DL) methods plays an important role in achieving state-of-the-art results in supervised, semi-supervised, and self-supervised image classification. In particular...
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ISBN:
(数字)9798350360325
ISBN:
(纸本)9798350360332
The application of data augmentation for deep learning (DL) methods plays an important role in achieving state-of-the-art results in supervised, semi-supervised, and self-supervised image classification. In particular, channel transformations (e.g., solarize, grayscale, brightness adjustments) are integrated into data augmentation pipelines for remote sensing (RS) image classification tasks. However, contradicting beliefs exist about their proper applications to RS images. A common point of critique is that the application of channel augmentation techniques may lead to physically inconsistent spectral data (i.e., pixel signatures). To shed light on the open debate, we propose an approach to estimate whether a channel augmentation technique affects the physical information of RS images. To this end, the proposed approach estimates a score that measures the alignment of a pixel signature within a time series that can be naturally subject to deviations caused by factors such as acquisition conditions or phenological states of vegetation. We compare the scores associated with original and augmented pixel signatures to evaluate the physical consistency. Experimental results on a multi-label image classification task show that channel augmentations yielding a score that exceeds the expected deviation of original pixel signatures can not improve the performance of a baseline model trained without augmentation.
Radiomics-based researches have shown the predictive abilities of machine learning methods in medical diagnosis. However, different machine learning approaches affect the prediction performance. This paper proposes a ...
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Amid the relentless battle against the COVID-19 pandemic, a cadre of researchers and scientists is diligently crafting innovative tools to expedite diagnosis and treatment. One standout innovation is the integration o...
Amid the relentless battle against the COVID-19 pandemic, a cadre of researchers and scientists is diligently crafting innovative tools to expedite diagnosis and treatment. One standout innovation is the integration of radiographic images and deep convolutional neuronal networks (CNN), offering deeper insights into COVID-19. Early, accurate diagnosis is paramount in managing the COVID-19 crisis. It not only facilitates timely treatment but also curbs the virus's spread. The amalgamation of radiographic images and advanced machine learning techniques emerges as a transformative force. Traditional COVID-19 diagnostics rely heavily on molecular tests, like Polymerase Chain Reaction (PCR) tests. While highly accurate, these tests often involve protracted processing times and substantial resource demands. They may also produce false negatives in early infections. Radiographic imaging, especially chest X-rays, addresses these limitations. Researchers have harnessed deep convolutional neuronal networks, specialized for image analysis, to create an innovative diagnostic platform. This platform meticulously scrutinizes chest X-ray images, distinguishing COVID-19 pneumonia from other forms with exceptional precision. The deep CNN model undergoes intensive training on a diverse dataset, including chest X-rays from both COVID-19-positive and COVID-19-negative patients. It employs intricate processes of feature extraction and pattern recognition, discerning subtle nuances indicative of COVID-19-related lung abnormalities. This proficiency empowers the model to categorize new chest X-ray images, assisting radiologists in decision-making. The merits of this groundbreaking approach are multifaceted. Foremost is the substantial reduction in diagnosis time. Unlike molecular tests, which can take hours, chest X-ray analysis yields results within minutes, facilitating swift patient triage. It holds particular promise in resource-constrained settings where molecular testing may be scarc
The development of learning-based hyperspectral image (HSI) compression models has recently attracted significant interest. Existing models predominantly utilize convolutional filters, which capture only local depende...
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Learning distance functions between complex objects, such as the Wasserstein distance to compare point sets, is a common goal in machine learning applications. However, functions on such complex objects (e.g., point s...
Learning distance functions between complex objects, such as the Wasserstein distance to compare point sets, is a common goal in machine learning applications. However, functions on such complex objects (e.g., point sets and graphs) are often required to be invariant to a wide variety of group actions e.g. permutation or rigid transformation. Therefore, continuous and symmetric product functions (such as distance functions) on such complex objects must also be invariant to the product of such group actions. We call these functions symmetric and factor-wise group invariant functions (or SFGI functions in short). In this paper, we first present a general neural network architecture for approximating SFGI functions. The main contribution of this paper combines this general neural network with a sketching idea to develop a specific and efficient neural network which can approximate the p-th Wasserstein distance between point sets. Very importantly, the required model complexity is independent of the sizes of input point sets. On the theoretical front, to the best of our knowledge, this is the first result showing that there exists a neural network with the capacity to approximate Wasserstein distance with bounded model complexity. Our work provides an interesting integration of sketching ideas for geometric problems with universal approximation of symmetric functions. On the empirical front, we present a range of results showing that our newly proposed neural network architecture performs comparatively or better than other models (including a SOTA Siamese Autoencoder based approach). In particular, our neural network generalizes significantly better and trains much faster than the SOTA Siamese AE. Finally, this line of investigation could be useful in exploring effective neural network design for solving a broad range of geometric optimization problems (e.g., k-means in a metric space).
AI-powered educational technologies are emerging as transformative forces in the quickly changing field of education, where innovation is essential to keeping ahead of the competition. Assessments are one area that is...
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ISBN:
(数字)9798331532420
ISBN:
(纸本)9798331532437
AI-powered educational technologies are emerging as transformative forces in the quickly changing field of education, where innovation is essential to keeping ahead of the competition. Assessments are one area that is undergoing revolutionary transformation. Artificial Intelligence (AI) is redefining the way we evaluate learning outcomes as traditional evaluation approaches struggle to keep up with the demands of the digital age. The field of artificial intelligence-powered learning assessments has a lot of promising future developments ahead of it. This research proposes novel technique in assessment and evaluation based on online education system using Artificial intelligence in machine learning techniques. Here, the input has been analysed as student performance data based on online education and processed for removing of missing values with noise removal. Then this data has been analysed using spatio LSTM convolutional fuzzy neural network for classification of evaluation and assessment model. the experimental analysis has been carried out for various student performance analysis dataset in terms of training accuracy, average precision, specificity, F-measure and recall. Proposed method training accuracy 97%, Average precision 94%, F-measure 90%, RECALL 93%, SPECIFICITY 96%.
Cryptocurrency crime incidents in Ethereum are continuously rising, with phishing scams accounting for 50% of all criminal activities. The severe data imbalance significantly impacts the performance of Ethereum phishi...
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ISBN:
(数字)9798350349184
ISBN:
(纸本)9798350349191
Cryptocurrency crime incidents in Ethereum are continuously rising, with phishing scams accounting for 50% of all criminal activities. The severe data imbalance significantly impacts the performance of Ethereum phishing detection models. The current solution may introduce redundant information or lead to the loss of important data. In this paper, we propose an Ethereum phishing detection method based on Graph Contrastive Learning with augmentations. This approach addresses the issue of insufficient learning of phishing node features, thus alleviating the influence of data imbalance on the model’s detection performance without disrupting the original data distribution. To enhance the representation of structural features, we employ two data augmentation methods: feature masking and edge perturbation. We conducted extensive experiments on a real Ethereum phishing dataset to evaluate the performance of our method. Compared to alternative methods, our approach not only significantly improves Precision, ranging from 12% to 30%, but also achieves noticeable enhancements in Recall, Auc, and F1-score. The experimental results provide ample evidence of the effectiveness of the proposed method.
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