Fast 3D Visualization of Massive Geological Data Based on Clustering Index Fusion

Zhang, Yuhang; Wen, Chang; Xie, Kai; Zhang, Min; He, Jian-Biao

doi:10.1109/access.2022.3157823

Cited by 17 publications

(12 citation statements)

References 33 publications

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“…Subsequently, the two vectors are combined and fused to produce the final weight vector. In order to capture both prominent features and average characteristics across channels, a combination of global pooling and maximum pooling is utilized (Zhang Y.-H. et al, 2022 ). This approach allows the network to prioritize visible regions of pedestrians while retaining overall channel information (Zhang M. et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Chaotic medical image encryption method using attention mechanism fusion ResNet model

Peng

2023

Front. Neurosci.

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IntroductionWith the rapid advancement of artificial intelligence (AI) technology, the protection of patient medical image privacy and security has become a critical concern in current research on image privacy protection. However, traditional methods for encrypting medical images have faced criticism due to their limited flexibility and inadequate security. To overcome these limitations, this study proposes a novel chaotic medical image encryption method, called AT-ResNet-CM, which incorporates the attention mechanism fused with the ResNet model.MethodsThe proposed method utilizes the ResNet model as the underlying network for constructing the encryption and decryption framework. The ResNet's residual structure and jump connections are employed to effectively extract profound information from medical images and expedite the model's convergence. To enhance security, the output of the ResNet model is encrypted using a logistic chaotic system, introducing randomness and complexity to the encryption process. Additionally, an attention mechanism is introduced to enhance the model's response to the region of interest within the medical image, thereby strengthening the security of the encrypted network.ResultsExperimental simulations and analyses were conducted to evaluate the performance of the proposed approach. The results demonstrate that the proposed method outperforms alternative models in terms of encryption effectiveness, as indicated by a horizontal correlation coefficient of 0.0021 and information entropy of 0.9887. Furthermore, the incorporation of the attention mechanism significantly improves the encryption performance, reducing the horizontal correlation coefficient to 0.0010 and increasing the information entropy to 0.9965. These findings validate the efficacy of the proposed method for medical image encryption tasks, as it offers enhanced security and flexibility compared to existing approaches.DiscussionIn conclusion, the AT-ResNet-CM method presents a promising solution to address the limitations of traditional encryption techniques in protecting patient medical images. By leveraging the attention mechanism fused with the ResNet model, the method achieves improved security and flexibility. The experimental results substantiate the superiority of the proposed method in terms of encryption effectiveness, horizontal correlation coefficient, and information entropy. The proposed method not only addresses the shortcomings of traditional methods but also provides a more robust and reliable approach for safeguarding patient medical image privacy and security.

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Section: Methodsmentioning

confidence: 99%

Chaotic medical image encryption method using attention mechanism fusion ResNet model

Peng

2023

Front. Neurosci.

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“…Thus, obtaining high-level domain-invariant feature representations for EEG signals is desirable. Deep-learning methods provide a potential solution by producing high-level domain-invariant features and achieving high-accuracy classification results for EEG signals [29]. Representative deep-learning methods such as the RNN, LSTM, DBN, and CNN have demonstrated strong feature-learning abilities and outstanding performance in object detection and classification tasks [30,31].…”

Section: Eeg-based Fatigue Detection Using Deep Learningmentioning

confidence: 99%

Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network

Xu,

Li,

Feng

2023

Sensors

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Fatigue of miners is caused by intensive workloads, long working hours, and shift-work schedules. It is one of the major factors increasing the risk of safety problems and work mistakes. Examining the detection of miner fatigue is important because it can potentially prevent work accidents and improve working efficiency in underground coal mines. Many previous studies have introduced feature-based machine-learning methods to estimate miner fatigue. This work proposes a method that uses electroencephalogram (EEG) signals to generate topographic maps containing frequency and spatial information. It utilizes a convolutional neural network (CNN) to classify the normal state, critical state, and fatigue state of miners. The topographic maps are generated from the EEG signals and contrasted using power spectral density (PSD) and relative power spectral density (RPSD). These two feature extraction methods were applied to feature recognition and four representative deep-learning methods. The results showthat RPSD achieves better performance than PSD in classification accuracy with all deep-learning methods. The CNN achieved superior results to the other deep-learning methods, with an accuracy of 94.5%, precision of 97.0%, sensitivity of 94.8%, and F1 score of 96.3%. Our results also show that the RPSD–CNN method outperforms the current state of the art. Thus, this method might be a useful and effective miner fatigue detection tool for coal companies in the near future.

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“…However, there are still some challenges such as the evaluation of encryption strength [31,32], robustness to different types of attacks [33] and adaptability of the algorithms to large-scale images [34][35][36]. Future research can address these issues and propose more efficient and secure CNN encryption algorithms [24,30,37,38].…”

Section: Related Workmentioning

confidence: 99%

Image Encryption Algorithm Combining Chaotic Image Encryption and Convolutional Neural Network

Feng

et al. 2023

Electronics

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With the rapid development of information technology, the security of images has emerged as a significant area of research. This study presents an algorithm that integrates chaotic image encryption and a convolutional neural network (CNN) to enhance security and efficiency. The algorithm applies the properties of randomness and nonlinear mapping of chaotic sequences with the advanced feature extraction capabilities of a CNN model to achieve robust image encryption. First, we outline the fundamentals of chaotic image encryption and CNN. Chaotic image encryption employs chaotic sequence generation and nonlinear mapping to scramble pixel values for encryption purposes, while a CNN, as a deep-learning model with a local perceptual field and weight sharing, effectively extracts high-level image features. Subsequently, we provide a detailed description of the specific steps involved in combining chaotic image encryption and the CNN. These steps include chaotic sequence generation, pixel value mapping, feature extraction, and key management. The algorithm achieves high-strength encryption by performing dissimilarity operations between the chaotic sequence and image pixel values, as well as extracting high-level image features using the CNN. Finally, we conduct experimental evaluations of the algorithm and compare it with traditional chaotic image encryption methods. The experimental results demonstrate that the image encryption algorithm exhibits significant improvements in encryption quality and security while offering advantages in computational performance and encryption/decryption speed.

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Fast 3D Visualization of Massive Geological Data Based on Clustering Index Fusion

Cited by 17 publications

References 33 publications

Chaotic medical image encryption method using attention mechanism fusion ResNet model

Chaotic medical image encryption method using attention mechanism fusion ResNet model

Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network

Image Encryption Algorithm Combining Chaotic Image Encryption and Convolutional Neural Network

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