With the explosive growth of multimodal Internet data, cross-modal hashing retrieval has become crucial for semantically searching instances across different modalities. However, existing cross-modal retrieval methods...
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As a significant application of machine learning in financial scenarios, loan default risk prediction aims to evaluate the client’s default probability. However, most existing deep learning solutions treat each appli...
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Cardiovascular disease remains a major issue for mortality and morbidity, making accurate classification crucial. This paper introduces a novel heart disease classification model utilizing Electrocardiogram (ECG) sign...
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With the widely use of mobile consumer electronics devices, location-based services becomes more and more popular in our lives, e.g., mapping services and ride-hailing services. Most of location-based services rely on...
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With the widely use of mobile consumer electronics devices, location-based services becomes more and more popular in our lives, e.g., mapping services and ride-hailing services. Most of location-based services rely on the support of efficient and accurate route planning. However, existing route planning algorithms mainly aim to plan for a single query in dynamic road networks, while ignoring the internal flows caused by massive planned route themselves, i.e., many vehicles may take the same road segments and thus cause traffic congestion and increase the global travel time. Therefore, in this paper, we focus on massive route planning in dynamic road networks to avoid such traffic congestion caused by the internal traffic flows. We first formally define the massive route planning with minimizing the global travel time (MRP-GTT) problem. Then, we prove that the MRP-GTT problem is NP-hard. To effectively solve it, we first design a novel game theory based algorithm (GTA) to reduce the global travel time for massive route queries. Because of the low efficiency of the global gaming for all queries, we then devise a game theory with query clustering algorithm (GTA-QC) in the paper, which first clusters queries based on the source and destination locations of queries, so that only queries in the same cluster can participate in a game to improve gaming efficiency. Extensive experiments on both synthetic and real datasets demonstrate the efficiency and effectiveness of our algorithms. IEEE
Introduction: Remote data exchange operations in healthcare are observed, consult-ed, monitored and treated by the Internet of Medical Things (IoMT). It is an extension of the Internet of Things (IoT). Method: At the ...
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X-ray security inspection for detecting prohibited items is widely used to maintain social order and ensure the safety of people’s lives and property. Due to the large number of parameters and high computational comp...
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The Social Internet of Things (SIoT) is an innovative fusion of IoT and smart devices that enable them to establish dynamic relationships. Securing sensitive data in a smart environment requires a model to determine t...
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In today's environment, people rely on online commerce for almost everything. Online business has many advantages such as ease of use, efficiency, fast payment, etc. It has many advantages, such as, but also prote...
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The use of metamaterial enhances the performance of a specific class of antennas known as metamaterial *** radiation cost and quality factor of the antenna are influenced by the size of the *** antennas allow for the ...
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The use of metamaterial enhances the performance of a specific class of antennas known as metamaterial *** radiation cost and quality factor of the antenna are influenced by the size of the *** antennas allow for the circumvention of the bandwidth restriction for small *** parameters have recently been predicted using machine learning algorithms in existing *** learning can take the place of the manual process of experimenting to find the ideal simulated antenna *** accuracy of the prediction will be primarily dependent on the model that is *** this paper,a novel method for forecasting the bandwidth of the metamaterial antenna is proposed,based on using the Pearson Kernel as a standard *** with these new approaches,this paper suggests a unique hypersphere-based normalization to normalize the values of the dataset attributes and a dimensionality reduction method based on the Pearson kernel to reduce the dimension.A novel algorithm for optimizing the parameters of Convolutional Neural Network(CNN)based on improved Bat Algorithm-based Optimization with Pearson Mutation(BAO-PM)is also presented in this *** prediction results of the proposed work are better when compared to the existing models in the literature.
In recent years due to increase in the number of customers and organizations utilize cloud applications for personal and professionalization become greater. As a result of this increase in utilizing the Cloud services...
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