In this paper, three convolutional neural network models are used to achieve end-to-end recognition of blood cell images. The network model parameters are initialized by transfer learning from the pre-trained model on ImageNet, and then the blood cell images are input into the model, and the network model training is completed by back-propagation to continuously update the parameters. For small-scale datasets, the number of blood cell images is expanded using data increments to improve the generalization ability of the model. Experimental results on the BCCD dataset show that the best result MobileNetV2 achieves an accuracy and precision of 0.894 and 0.916, respectively.
To address the current problem of frequent piracy infringement of digital images, this paper proposes an image watermarking algorithm based on DWT and DCT. Firstly, the binary watermark is encrypted by the logistic chaos algorithm, which produces a chaotic sequence with better uncertainty and initial value sensitivity; secondly, the Arnold transform is applied to the encrypted watermarked image to eliminate the correlation between the watermarked pixels, so that the robustness of the image information is improved; the original image is decomposed by the second-level wavelet in the embedding process, and then the low-frequency sub-band LL1 is blocked In the embedding process, the original image is decomposed by the second-level wavelet, and then the low-frequency sub-band LL1 is chunked, and the pre-processed watermark information is embedded by modifying the IF coefficients through DCT transform. In this paper, Matlab simulation experiments are conducted for this algorithm, and the experimental results have good invisibility and good robustness when subjected to various malicious attacks, which is practical for safeguarding digital image copyright.
In recent years, sustainable development has attracted more and more attention, and waste classification is a social problem related to people's livelihood and social sustainable development. Therefore, this paper proposes a waste classification system based on deep learning. The system is built with tensorflow framework in deep learning and data training with mobilenet model, The data set is obtained by the method of web crawler, cleaned and classified, and the graphical interface is constructed by pyqt5. Finally, by uploading the pictures, the system can effectively classify the objects in the pictures into the correct garbage name and category.
The Transformer structures have achieved excellent performances in an increasing number of fields. In the field of natural language processing, the original Transformer structure was used to work on a full connectivity graph, which depicts all connections between words in a phrase. It has been subsequently applied in areas such as computer vision. Recently, many studies have attempted to apply Transformer structures to graph data and achieved good performance. However, it is still a challenge to embedding the structural information of the graph data. To understand the representation of graph data, we employed three distinct ways of embedding the positional and structural information of graph data based on the Transformer structure in this work. To learn the feature representation of the graph data, the encoded structural information and original feature information of the graph data were fed into the Transformer structure. For the learned feature representations, we used them for node classification and graph regression, and then compared and analyzed the three different encoding methods.
KEYWORDS: Social networks, Data mining, Detection and tracking algorithms, Analytical research, Data modeling, Social network analysis, Mining, Information and communication technologies
With the rapid development of communication technology and social media, people are constantly interacting with each other, generating a large amount of behavioral data and forming a complex social network. With time, the interaction behaviors between people also change, which results in a constantly changing social network. Analysis of evolutionary social networks can effectively mining people's behavioral data, understanding people's behavior models in depth. Community detection is one of the important methods for social network analysis, and evolutionary analysis of dynamically changing communities, especially the core stable community structure in dynamic networks, can reveal the community structure at different periods and reveal the social network evolution patterns. In this paper, we define communities on real complex networks as core-edge structures with a tight core and sparse periphery, which can represent the importance of nodes within the community. And we propose a community detection algorithm based on the core-edge structure communities. Moreover, based on the characteristics of the core-edge community structure, we propose a community similarity calculation method based on the importance of members and an evolution analysis method based on the core-edge community structure.
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