The unprecedented Coronavirus Disease 2019 had spread and diffused in many countries and regions worldwide, posing a serious threat to people's health and lives. This review presents a systematic analysis and summ...
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To assist blind people in travelling and solve the problems of high hardware costs, insufficient portability of blind assistance devices, and vulnerability to environmental impacts, a modified YOLOv5 algorithm (YOLOv5...
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Computed Tomography (CT) images are essential for visualizing the internal structures of the human body and are widely applied in the diagnosis of various diseases. However, during the processes of generating, storing...
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ISBN:
(数字)9798331540043
ISBN:
(纸本)9798331540050
Computed Tomography (CT) images are essential for visualizing the internal structures of the human body and are widely applied in the diagnosis of various diseases. However, during the processes of generating, storing, and transmitting medical CT images, the clarity of these images can be degraded by non-human factors, resulting in issues such as reduced resolution and blurred texture details, thereby compromising image fidelity. In this study, we present an approach utilizing Generative Adversarial Networks (GANs). that incorporates a grayscale value loss as an additional constraint while maintaining the original loss function constraints. We conduct experiments for 4x super-resolution reconstruction. To evaluate the reconstruction performance, we compare the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index (SSIM) of the reconstructed images. The experimental results demonstrate ,which the proposed method exhibits promising performance in both quantitative metrics and visual quality.
To address the issue of the conventional D-S combination rule not effectively dealing with highly conflicting evidence fusion, a conflicting evidence fusion algorithm utilizing a greedy strategy is proposed. First, by...
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ISBN:
(数字)9798331540043
ISBN:
(纸本)9798331540050
To address the issue of the conventional D-S combination rule not effectively dealing with highly conflicting evidence fusion, a conflicting evidence fusion algorithm utilizing a greedy strategy is proposed. First, by adopting a greedy strategy, the effective evidence distance is defined based on Jousselme’s evidence distance, followed by the definition of strongly and weakly correlated evidence. The average value of the effective evidence distance is calculated, and based on this the evidence is classified. Finally, weights are calculated individually for the classified evidence. Weights are then reassigned to the two types of evidence, and the corrected evidence is fused to obtain the final fusion result. Numerical examples are provided to demonstrate the effectiveness of the method proposed in this paper in addressing the limitations of the conventional D-S combination rule for fusing highly conflicting evidence.
This essay introduces a new approach to address the challenges of applying Dempster-Shafer evidence theory to tackling high-conflict evidence. We first employed Jousselme’s distance and compatibility coefficients to ...
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ISBN:
(数字)9798331540043
ISBN:
(纸本)9798331540050
This essay introduces a new approach to address the challenges of applying Dempster-Shafer evidence theory to tackling high-conflict evidence. We first employed Jousselme’s distance and compatibility coefficients to measure confidence and support, then converted these values into vectors. Next, we applied game theory to convert the vectors into a system of linear equations,from which we obtained a corresponding combination of weight coefficients that we used to modify the initial evidence. Finally, we applied the Dempster-Shafer combination rule to achieve evidence fusion. A case analysis shows that our proposed method provides more sensible fusion results for high-conflict evidence.
In order to draw and analyze the current status of the research on the application of artificial intelligence in the field of health in China and abroad, we explored the hotspots and frontiers of this research field a...
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Drama education activities carried out by traditional culture comply with, guide and stimulate children's dramatic nature so that each child can find a suitable place for himself in drama education activities and ...
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The attack behavior in the network poses a threat to security and privacy issues. In order to improve the effectiveness of IoT network attack detection and compensate for the shortcomings of traditional machine learni...
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ISBN:
(数字)9798331540043
ISBN:
(纸本)9798331540050
The attack behavior in the network poses a threat to security and privacy issues. In order to improve the effectiveness of IoT network attack detection and compensate for the shortcomings of traditional machine learning methods in detection capabilities, a network intrusion detection method based on lightweight convolutional neural networks (CNN) and bidirectional long short-term memory networks (BiLSTM) is proposed. This method preprocesses data through normalization techniques, extracts data features from both spatial and temporal dimensions using CNN and BiLSTM models, and inputs the extracted spatiotemporal feature vectors into the CNN BiLSTM upper classifier for training, achieving intrusion detection. The proposed model was validated on the dataset UNSW-NB15, and the results showed that the binary and multi classification accuracy of the CNN_BiLSTM model was around 93%. Compared with other methods, the proposed method significantly improves the accuracy of network intrusion detection, and also demonstrates better performance in detection accuracy and false positive rate.
Lactate,as a metabolite,plays a significant role in a number of fields,including medical diagnostics,exercise physiology and food *** methods for lactate measurement often involve expensive and cumbersome *** study de...
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Lactate,as a metabolite,plays a significant role in a number of fields,including medical diagnostics,exercise physiology and food *** methods for lactate measurement often involve expensive and cumbersome *** study developed a portable and low-cost lactate measurement system,including independently detectable hardware circuits and user-friendly embedded software,computer,and smartphone *** experiment verified that the relative error of the detection current in the device circuit was less than 1%.The electrochemical performance was measured by comparing the[Fe(CN)_(6)]^(3−)/[Fe(CN)_(6)]^(4−)solution with the desktop electrochemical workstation CHI660E,and a nearly consistent chronoamperometry(CA)curve was *** modified lactate sensors were used for CA testing of *** the concentration range of 0.1 mmol·L^(−1)to 20 mmol·L^(−1),there was a good linear relationship between lactate concentration and steady-state current,with a correlation coefficient(R2)greater than 0.99 and good repeatability,demonstrating the reliability of the developed *** lactate measurement system developed in this study not only provides excellent detection performance and reliability,but also achieves portability and low cost,providing a new solution for lactate measurement.
Recent advancements in CNNs for medical image segmentation have focused on the combination of CNN and Transformer architectures to capture both local and global features. However, challenges remain, such as effectivel...
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ISBN:
(数字)9798331517021
ISBN:
(纸本)9798331517038
Recent advancements in CNNs for medical image segmentation have focused on the combination of CNN and Transformer architectures to capture both local and global features. However, challenges remain, such as effectively utilizing fused features at the encoder-decoder bottleneck and addressing blurred object edges in datasets. To tackle these issues, we introduce a multi-scale channel adaptive sensing module, enhancing the model's representational and perceptual capabilities by integrating multi-scale and channel-specific information. Additionally, we propose a cross-perception module and multi-scale segmentation head to improve global coordination and multi-scale information gathering. Experiments on the ACDC and Synapse datasets demonstrate our model's superiority.
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