It has very realistic significance for improving the quality of users＇ accessing information to filter and selectively retrieve the large number of information on the Internet. On the basis of analyzing the existing ...
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It has very realistic significance for improving the quality of users＇ accessing information to filter and selectively retrieve the large number of information on the Internet. On the basis of analyzing the existing users＇ interest models and some basic questions of users＇ interest （representation, derivation and identification of users＇ interest）, a Bayesian network based users＇ interest model is given. In this model, the users＇ interest reduction algorithm based on Markov Blanket model is used to reduce the interest noise, and then users＇ interested and not interested documents are used to train the Bayesian network. Compared to the simple model, this model has the following advantages like small space requirements, simple reasoning method and high recognition rate. The experiment result shows this model can more appropriately reflect the user＇s interest, and has higher performance and good usability.
The application of the low-cost inertial measurement unit (IMU) in many fields is growing, but the related attitude algorithms have the problems of low precision and poor adaptability. In this paper, a novel attitude ...
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The integration of Internet of Things (IoT) technologies into Building Information Modelling (BIM) provides significant opportunities to support useful services and functionalities for end users in buildings, enabling...
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In order to process a hand drawn input graph and digitize it, various digital geometric techniques have been used. These techniques utilize the inherent combinatorial properties of the relative arrangement of the obje...
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On the Internet, every different day, kinds of attacks are deployed on innocent users. Among all, phishing is the most severe attack in which users lose their credentials or private information and their financial sta...
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Vehicular ad hoc networks (VANETs), formed by computers embedded in vehicles and the traffic infrastructure, are expected to develop in the near future to improve traffic safety and efficiency. To this end, VANETs sho...
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—Implementation of advanced intelligent deep learning techniques for electric vehicles (EVs) energy consumption analysis is obstructed by two main subjects. First, the problem of finding a very similar collection of ...
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—Implementation of advanced intelligent deep learning techniques for electric vehicles (EVs) energy consumption analysis is obstructed by two main subjects. First, the problem of finding a very similar collection of datasets to the actual EVs energy usage in terms of feature space and data distribution. Second, training a retrained model from scratch requires a massive amount of computational power;however, this does not guarantee to catch rare events included in datasets. To mitigate the aforementioned concerns, this article aims to present a model based on deep transfer learning (DTL) between domain-variant datasets, to reduce the need for the existence of a vast amount of EVs data, including driving characteristics and patterns. Also, this model applies a distributed cooperative learning approach to identify highly correlated energy consumption parameters by building the model on previously acquired knowledge from preceding learning phases in order to enhance the artificial intelligence (AI) accuracy level of the proposed energy management system. Impact Statement—The drastic growth in the conventional transportation system raises serious air pollution concerns. Eco-friendly vehicles, in contrast, have been introduced as an alternative to alleviate such environmental issues. The aforementioned switch requires proper infrastructure to increase the public’s interest before mass production. Ensuring EV owners’ satisfaction by increasing the quality of experience and desecrating range anxiety is the primary goal for EV producers. One attempt to reach this goal is to provide a precise remaining battery level for EVs based on users’ driving destinations and behaviors. However, achieving accurate battery estimation leveraging AI is limited to the amount of historical data. We, therefore, consider introducing an estimation model which trains insufficient EV data history by transferring previously trained knowledge to increase the precision of remaining battery level pred
The rapid proliferation of some real-time applications (e.g., video surveillance) has driven enormous interest in maximizing information freshness, quantified by the age of information (AoI). For some computation-inte...
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Ethics in AI becomes a global topic of interest for both policymakers and academic researchers. In the last few years, various research organizations, lawyers, think tankers, and regulatory bodies get involved in deve...
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