Context‐Aware Cloud Service Selection Model for Mobile Cloud Computing Environments

Wu, Xu

doi:10.1155/2018/3105278

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…After working for a period of time, the network can reach a relatively stable state. Compared with feedforward neural network, it has higher complexity [17,18]. (1) Gaussian function (Gaussian function)…”

Section: Methodsmentioning

confidence: 99%

Point Cloud Data Hole Repair Aggregation Algorithm Based on Optimized Neural Network

2022

Wireless Communications and Mobile Computing

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In order to solve the problem of cost cloud data and hole repair efficiency and accuracy, this article offers a study of integrated cloud network hole algorithm research based on optimal neural network. This paper first introduces a common point cloud hole-filling algorithm, provides a neural network-based point cloud blank filling algorithm, and introduces hotspot problems in a given algorithm, combined with the technology related to high-performance computing, the parallel optimization technology based on OpenMP and CUDA is adopted to accelerate the algorithm accordingly. Experiments have shown that the accuracy of pre- and postoptimization of CUDA-based algorithms varies. As the model and input point cloud data increase, the accuracy of the algorithm decreases slightly. However, when the data increases to 104 orders of magnitude, the rate of accuracy decline meets an inflection point and finally stabilizes to about 96.8% in the environment of 132457 data of monk model. The point cloud hole-filling algorithm based on the optimal neural network given in this article is highly predictable, can well repair incomplete point cloud holes, has good repair effect on point cloud holes, and can obtain high acceleration ratio, which can provide reference for practical engineering application.

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Section: Methodsmentioning

confidence: 99%

Point Cloud Data Hole Repair Aggregation Algorithm Based on Optimized Neural Network

2022

Wireless Communications and Mobile Computing

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“…In the fog cloud environment, QoS information is normally collected and stored in various fog servers, instead of being transferred to the remote cloud directly, due to the big volume of data and heavy transmission cost. In this situation, QoS information is always distributed but not centralized [9], which means QoS information is often sparse and unavailable for mobile users. Therefore, motivated by making an effective recommendation, it is a feasible way to complete missing QoS values by making predictions.…”

Section: Introductionmentioning

confidence: 99%

Using NearestGraph QoS Prediction Method for Service Recommendation in the Cloud

Ding

Seid

2018

Wireless Communications and Mobile Computing

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With the advent of the mobile network, the fusion of cloud computing and fog computing is becoming feasible to promise lower latency and short-fat connection. However, there are a lot of redundant cloud-aware services with identical functionalities but a different quality of service (QoS) in the fog cloud environment. In fact, since QoS information is stored in distributed fog servers rather than remote cloud, it is hard for individuals to make recommendation and selection with sparse QoS information. Collaborative filtering is an important method for the sparsity problems and has been widely adopted on the prediction of missing QoS values. Focusing on the fact that existing researchers often ignore the QoS fluctuation in a wide range in the fog cloud environment, a novel neighbor-based QoS prediction method is proposed for service recommendation, in which a concept and calculation method is put forward to describe the stable status of services and users with quantifiable QoS values, and a NearestGraph algorithm is further designed to recognize stable or unstable candidate along with their popularity by a nearest neighbor graph structure which can help to make missing QoS values prediction in a certain order to improve final prediction accuracy. Experimental results confirm that the proposed method is effective in predicting unknown QoS values in terms of service recommendation accuracy and efficiency.

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A Comprehensive Review of Cloud Forensics and Blockchain Based Solutions

Pallavi

Bharti

2022

2022 6th International Conference on Electronics, Communication and Aerospace Technology

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Context‐Aware Cloud Service Selection Model for Mobile Cloud Computing Environments

Cited by 6 publications

References 22 publications

Point Cloud Data Hole Repair Aggregation Algorithm Based on Optimized Neural Network

Point Cloud Data Hole Repair Aggregation Algorithm Based on Optimized Neural Network

Using NearestGraph QoS Prediction Method for Service Recommendation in the Cloud

A Comprehensive Review of Cloud Forensics and Blockchain Based Solutions

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