In this paper, we try to systematically study how to perform doctor recommendation in medical social net- works （MSNs）. Specifically, employing a real-world medical dataset as the source in our work, we propose iBol...
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In this paper, we try to systematically study how to perform doctor recommendation in medical social net- works （MSNs）. Specifically, employing a real-world medical dataset as the source in our work, we propose iBole, a novel hybrid multi-layer architecture, to solve this problem. First, we mine doctor-patient relationships/ties via a time-constraint probability factor graph model （TPFG）. Second, we extract network features for ranking nodes. Finally, we propose RWR- Model, a doctor recommendation model via the random walk with restart method. Our real-world experiments validate the effectiveness of the proposed methods. Experimental results show that we obtain good accuracy in mining doctor-patient relationships from the network, and the doctor recommendation performance is better than that of the baseline algorithms： traditional Ranking SVM （RSVM） and the individual doctor recommendation model （IDR-Model）. The results of our RWR-Model are more reasonable and satisfactory than those of the baseline approaches.
Under the influence of multipath effects and small scale fading, the robustness and reliability of the existing human detection methods based on radio frequency signals are easy to be impaired. In this paper, we propo...
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ISBN:
(纸本)9781509056972
Under the influence of multipath effects and small scale fading, the robustness and reliability of the existing human detection methods based on radio frequency signals are easy to be impaired. In this paper, we propose a novel design that using the multi-layer filtering of channel state information (CSI) to identify moving targets in dynamic environments and analyze the gait periodicity of human. We employ an efficient CSI subcarrier feature difference to the multi-layer filtering method leveraging principal component analysis (PCA) and discrete wavelet transform (DWT) to eliminate the noises. Furthermore, we propose a profile matching mechanism for human detection and a periodicity analysis mechanism for human gait taking advantage of the above design. We evaluated it with the commodity Wi-Fi infrastructures in different environments. Experimental results indicate that our approach performs identification of human with an average accuracy of 94%.
RFID systems nowadays are operated at large-scale in terms of both occupied space and tag quantity. One may have prior knowledge of the complete set of tags (denoted by N) and any set of wanted tags (denoted by M) wit...
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ISBN:
(纸本)9781509032822
RFID systems nowadays are operated at large-scale in terms of both occupied space and tag quantity. One may have prior knowledge of the complete set of tags (denoted by N) and any set of wanted tags (denoted by M) within the complete set, i.e., M ⊆ N. Then here comes an open problem: when one is particularly interested in a subarea of the system, how to collect information (not simply tagIDs) from a wanted subset (denoted by d M ) of the interrogated tags (denoted by d N ) in that subarea? This issue has great significance in many practical applications but appears to be challenging when there is a stringent time constraint. In this work, we first establish the lower-bound of this problem, and show a straightforward polling solution. Then, we propose a novel polling protocol called LocP, which consists of two phases: the Tags-Filtering phase and the Ordering-and-Reporting phase. LocP employs Bloom Filter twice to significantly reduce the scale of candidate tags in the Tags-Filtering phase. In the Ordering-and-Reporting phase, tags determine their own transmission time-slots according to the allocation vectors iteratively broadcasted by the reader. LocP thus achieves a delicate tradeoff between time and polling accuracy. We conduct extensive simulations to evaluate the performance of LocP. The results demonstrate that LocP is highly efficient in terms of information collection time, leading to convincing applicability and scalability of large-scale RFID systems.
Consider traditional clustering algorithms seldom had a research on users’ visit behavior and content, and they cannot cluster users with similar visit behavior into a community easily. Behavior of user cannot cluste...
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Software behavior mining is a very meaningful work. Finding that desirable patterns can assist the program maintainers to comprehend the software adequately. Although the existing high utility pattern mining algorithm...
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With the explosive growth of wireless application, how to improve the spectrum efficiency as well as reduce the communication consumption is a hot topic of research. In this paper, we propose a novel energy saving str...
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It is a fundamental issue to find a small subset of influential individuals in a complex network such that they can spread information to the largest scope of nodes in the network. Informative functions in complex sof...
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Identifying influential nodes is an important issue in understanding the process of information diffusion in complex software networks. Researchers generally define functions as nodes, and relationship of function cal...
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It is significant for measuring the importance of nodes accurately to improve software stability and robustness in software network. A software execution directed network takes function as a node and relationship of f...
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In software execution network, PageRank and betweenness methods are used to determine the importance of nodes. The experiment results show that the differences between nodes are not strong and cannot reflect the softw...
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