Parallel association rules mining has been improved the efficiency of data mining, and meanwhile concerned with the privacy preserving problem. A simple and effective method of parallel association rules mining which ...
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Parallel association rules mining has been improved the efficiency of data mining, and meanwhile concerned with the privacy preserving problem. A simple and effective method of parallel association rules mining which based on privacy protection----parallel association rules mining algorithm with privacy preserving (PARMA-P) has been introduced in this paper. It could achieve effective concealment of frequent item-set and then the association rules by the means of using imported hash assignment strategy in frequent item sets of FP sub tree could be protected. It has been used in HRM of an enterprise and experiments show that the algorithm can be simple and effective in protection of data privacy.
The water problem in north China is serious. Agriculture water consumption is very high that led to a serious drop in water table. So research on real water saving in the region is very important. The study improved s...
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In this paper, we use the method proposed by Eisen to analyze the genomic data collected from the State keylaboratory of Reproductive Biology, Chinese Academy of sciences. The analysis shows that the clustering metho...
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In this paper, we use the method proposed by Eisen to analyze the genomic data collected from the State keylaboratory of Reproductive Biology, Chinese Academy of sciences. The analysis shows that the clustering metho...
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In this paper, we use the method proposed by Eisen to analyze the genomic data collected from the State keylaboratory of Reproductive Biology, Chinese Academy of sciences. The analysis shows that the clustering methods effectively group genes of similar functions.
With the emergence of edge computing, there’s a growing need for advanced technologies capable of real-time, efficient processing of complex data on edge devices, particularly in mobile health systems handling pathol...
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With the emergence of edge computing, there’s a growing need for advanced technologies capable of real-time, efficient processing of complex data on edge devices, particularly in mobile health systems handling pathological images. On edge computing devices, the lightweighting of models and reduction of computational requirements not only save resources but also increase inference speed. Although many lightweight models and methods have been proposed in recent years, they still face many common challenges. This paper introduces a novel convolution operation, Dynamic Scalable Convolution (DSC), which optimizes computational resources and accelerates inference on edge computing devices. DSC is shown to outperform traditional convolution methods in terms of parameter efficiency, computational speed, and overall performance, through comparative analyses in computer vision tasks like image classification and semantic segmentation. Experimental results demonstrate the significant potential of DSC in enhancing deep neural networks, particularly for edge computing applications in smart devices and remote healthcare, where it addresses the challenge of limited resources by reducing computational demands and improving inference speed. By integrating advanced convolution technology and edge computing applications, DSC offers a promising approach to support the rapidly developing mobile health field, especially in enhancing remote healthcare delivery through mobile multimedia communication.
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