The use of distributed clustering is an important method of solving large-scale data mining problems. There are still some problems associated with distributed clustering, such as a performance bottleneck on the maste...
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The use of distributed clustering is an important method of solving large-scale data mining problems. There are still some problems associated with distributed clustering, such as a performance bottleneck on the master node and network congestion caused by global broadcasting. This paper proposes a decentralized clustering method based on density clustering and the content-addressable network technique. It can form a cluster with excellent scalability and load balancing capabilities based on several surrounding nodes. In addition, a method is presented for optimizing the way clustering results are gathered in different application scenarios. Based on our extensive experiments, the proposed approach performs three times better than benchmark algorithms in terms of efficiency and has a stable expanding ratio of about 0.6 for large-scale data sets.
With the development and maturity of intelligent interactive devices, introduction of devices into classroom has become inevitable tendency in order to enhance the teaching effect. At present there exist two difficult...
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With the development and maturity of intelligent interactive devices, introduction of devices into classroom has become inevitable tendency in order to enhance the teaching effect. At present there exist two difficulties when using devices in engineering. It is hard to get cognitive state of students based on multiple monitoring devices. Moreover, how to interact with students using devices intelligently, personally and automatically is challenging in order to improve student's cognition. To solve the above problems, in the paper we design an intelligent management solution about multidimensional interactive devices and propose an active learning algorithm. We aim to improve the judgment and intervention of students' cognition. The extensive experiments demonstrate that the proposed method performs 21% and 43% better than benchmark algorithm, respectively in prediction accuracy and intervention effect for students' cognition.
The issues related to information security control engineering for an industrial company with '1C' software are considered in this paper. The article discusses the types of threats to the company's informa...
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Temporal convolutional autoencoder has an important value of application in time-series analysis. In the paper we aim to use temporal convolutional autoencoder to help find out abnormal stocks quickly in the scenario ...
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Temporal convolutional autoencoder has an important value of application in time-series analysis. In the paper we aim to use temporal convolutional autoencoder to help find out abnormal stocks quickly in the scenario of financial trading market. However, there are still two critical problems to solve during the application of temporal convolutional autoencoder. First, trading data of each stock are multidimensional time-series data, while classical temporal convolutional autoencoder only applies to one-dimension data. Second, stock trading data in a market are huge and their analysis consumes a long time, which contradicts the demand of quick decision in stock trading. To solve the above problems, we improve temporal convolutional autoencoder based on multidimensional sampling, convolution kernel generated by prior knowledge, temporal feature reuse, parallel training on clouds. All the techniques help temporal convolutional autoencoder find abnormal stocks quickly and well. Extended experiments demonstrate that our proposed temporal convolutional autoencoder could raise F1 score to more than seventy percent. The largest time efficiency of finding abnormal stocks can be increased by ninety percent as well.
The sole use of single modality data often fails to capture the complex heterogeneity among patients,including the variability in resistance to anti-HER2 therapy and outcomes of combined treatment regimens,for the tre...
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The sole use of single modality data often fails to capture the complex heterogeneity among patients,including the variability in resistance to anti-HER2 therapy and outcomes of combined treatment regimens,for the treatment of HER2-positive gastric cancer(GC).This modality deficit has not been fully considered in many ***,the application of artificial intelligence in predicting the treatment response,particularly in complex diseases such as GC,is still in its ***,this study aimed to use a comprehensive analytic approach to accurately predict treatment responses to anti-HER2 therapy or anti-HER2 combined immunotherapy in patients with HER2-positive *** collected multi-modal data,comprising radiology,pathology,and clinical information from a cohort of 429 patients:310 treated with anti-HER2 therapy and 119 treated with a combination of anti-HER2 and anti-PD-1/PD-L1 inhibitors *** introduced a deep learning model,called the Multi-Modal model(MuMo),that integrates these data to make precise treatment response *** achieved an area under the curve score of 0.821 for anti-HER2 therapy and 0.914 for combined ***,patients classified as low-risk by MuMo exhibited significantly prolonged progression-free survival and overall survival(log-rank test,P<0.05).These findings not only highlight the significance of multi-modal data analysis in enhancing treatment evaluation and personalized medicine for HER2-positive gastric cancer,but also the potential and clinical value of our model.
Accurately restoring topology is both challenging and crucial in tubular structure extraction tasks, such as blood vessel segmentation and road network extraction. Diverging from traditional approaches based on pixel-...
The issues related to information security control engineering for an industrial company with “1C” software are considered in this paper. The article discusses the types of threats to the company's information r...
The issues related to information security control engineering for an industrial company with “1C” software are considered in this paper. The article discusses the types of threats to the company's information resources, including collective knowledge stored on electronic media. The paper contains standards and other documents governing the development and operation of an industrial company's information security. A brief description of the 1C software platform and 1C ERP is given. To prevent unauthorized access to the IT infrastructure and misappropriation of information important to the client, a set of needed organizational and technical measures with the use of 1C software products is considered. Some practical conclusions are also given.
With the explosive growth of information, recommendation systems have emerged to alleviate the problem of information overload. In order to improve the performance of recommendation systems, many existing methods intr...
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