scholarly journals Tourism Destination Preference Prediction Based on Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bin Deng ◽  
Jun Xu ◽  
Xin Wei
Keyword(s):  
Link Prediction ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Edge Computing ◽  
Prediction Methods ◽  
Random Forest Algorithm ◽  
Tourism Destination ◽  
Prediction Time ◽  
Weight Value ◽  
Destination Selection

In view of the fact that the important characteristics of tourism destination selection preference are not considered in the current prediction methods of tourism destination selection preference, resulting in low prediction accuracy and comprehensive accuracy and long prediction time, a tourism destination selection preference prediction method based on edge calculation is proposed. This paper uses edge computing to construct the characteristics of tourism destination selection preference and uses a random forest algorithm to select important features and carry out preliminary estimation and ranking. Using the multiple logit selection model, the tourists’ preference sequence for tourism destination selection is obtained and sorted and the tourism destination selection preference model is obtained. By calculating the weight value of tourism destination selection preference, the weight set of tourism destination selection preference is determined and the tourism destination selection preference is determined according to the link prediction method to realize the tourism destination selection preference prediction. The experimental results show that the comprehensive accuracy of the proposed method is good, which can effectively improve the prediction accuracy of tourism destination selection preference and shorten the prediction time of tourism destination selection preference.

Similarity indices based on link weight assignment for link prediction of unweighted complex networks

2017 ◽  
Vol 31 (02) ◽  
pp. 1650254 ◽  
Author(s):  
Shuxin Liu ◽  
Xinsheng Ji ◽  
Caixia Liu ◽  
Yi Bai
Keyword(s):  
Complex Networks ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Prediction Methods ◽  
Close Attention ◽  
Common Neighbor ◽  
Link Weight ◽  
Similarity Indices ◽  
Clustered Networks ◽  
Local Path

Many link prediction methods have been proposed for predicting the likelihood that a link exists between two nodes in complex networks. Among these methods, similarity indices are receiving close attention. Most similarity-based methods assume that the contribution of links with different topological structures is the same in the similarity calculations. This paper proposes a local weighted method, which weights the strength of connection between each pair of nodes. Based on the local weighted method, six local weighted similarity indices extended from unweighted similarity indices (including Common Neighbor (CN), Adamic-Adar (AA), Resource Allocation (RA), Salton, Jaccard and Local Path (LP) index) are proposed. Empirical study has shown that the local weighted method can significantly improve the prediction accuracy of these unweighted similarity indices and that in sparse and weakly clustered networks, the indices perform even better.

scholarly journals Hybrid prediction method of ECG signals based on VMD, PSR, and RBF neural network

2020 ◽  
Author(s):  
Fuying Huang ◽  
Tuanfa Qin ◽  
Limei Wang ◽  
Haibin Wan
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Delay Time ◽  
Prediction Accuracy ◽  
Rbf Neural Network ◽  
Prediction Method ◽  
Prediction Methods ◽  
Embedding Dimension ◽  
Ecg Signals ◽  
Ecg Data

Abstract Background: It is significant for doctors and body area networks (BANs) to predict ECG signals accurately. At present, the prediction accuracy of many existing ECG prediction methods is generally low. In order to improve the prediction accuracy of ECG signals in BANs, a hybrid prediction method of ECG signals is proposed in this paper. Methods: The proposed prediction method combines variational mode decomposition (VMD), phase space reconstruction (PSR), and a radial basis function (RBF) neural network. First, the embedding dimension and delay time of PSR are calculated according to the trained set of ECG data. Second, the ECG data are decomposed into several intrinsic mode functions (IMFs). Third, the phase space of each IMF is reconstructed according to the embedding dimension and the delay time. Fourth, an RBF neural network is established and each IMF is predicted by the network. Finally, the prediction results of all IMFs are added to realize the final prediction result. Results: To evaluate the prediction performance of the proposed method, simulation experiments are carried out on ECG data from the MIT-BIH Arrhythmia Database. The experimental results show that the prediction index RMSE (root mean square error) of the proposed method is only 10-3 magnitude and that of some traditional prediction methods is 10-2 magnitude.Conclusions: Compared with some traditional prediction methods, the proposed method improves the prediction accuracy of ECG signals obviously.

Prediction of missing links and reconstruction of complex networks

2016 ◽  
Vol 27 (10) ◽  
pp. 1650120 ◽  
Author(s):  
Cheng-Jun Zhang ◽  
An Zeng
Keyword(s):  
Complex Networks ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
High Performance ◽  
Point Of View ◽  
Prediction Methods ◽  
Online Systems ◽  
Network Properties ◽  
High Prediction ◽  
Simple Link

Predicting missing links in complex networks is of great significance from both theoretical and practical point of view, which not only helps us understand the evolution of real systems but also relates to many applications in social, biological and online systems. In this paper, we study the features of different simple link prediction methods, revealing that they may lead to the distortion of networks’ structural and dynamical properties. Moreover, we find that high prediction accuracy is not definitely corresponding to a high performance in preserving the network properties when using link prediction methods to reconstruct networks. Our work highlights the importance of considering the feedback effect of the link prediction methods on network properties when designing the algorithms.

LOCAL RANDOM WALK WITH DISTANCE MEASURE

2013 ◽  
Vol 27 (08) ◽  
pp. 1350055 ◽  
Author(s):  
BAOMIN XU ◽  
TINGLIN XIN ◽  
YUNFENG WANG ◽  
YANPIN ZHAO
Keyword(s):  
Random Walk ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Distance Measure ◽  
Prediction Method ◽  
Distance Distribution ◽  
Destination Node ◽  
Initial Node ◽  
Shortest Distance ◽  
First Time

Link prediction based on random walks has been widely used. The existing random walk algorithms ignore the probability of a walker visit from the initial node to the destination node for the first time, which makes a major contribution to establish links in some networks. To deal with the problem, we develop a link prediction method named Local Random Walk with Distance (LRWD) based on local random walk and the shortest distance of node pairs. In LRWD, walkers walk with their own steps rather than uniform steps. To evaluate the performance of the LRWD algorithm, we present the concept of distance distribution. The experimental results show that LRWD can improve the prediction accuracy when the distance distribution of the network is relatively concentrated.

scholarly journals Study on a New Method of Link-Based Link Prediction in the Context of Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Chen Jicheng ◽  
Chen Hongchang ◽  
Li Hanchao
Keyword(s):  
Big Data ◽  
Supervised Learning ◽  
Link Prediction ◽  
Variance Reduction ◽  
Prediction Method ◽  
Accurate Information ◽  
Prediction Methods ◽  
Sparse Networks ◽  
Core Issues ◽  
Present World

Link prediction is a concept of network theory that intends to find a link between two separate network entities. In the present world of social media, this concept has taken root, and its application is seen through numerous social networks. A typical example is 2004, 4 February “TheFeacebook,” currently known as just Facebook. It uses this concept to recommend friends by checking their links using various algorithms. The same goes for shopping and e-commerce sites. Notwithstanding all the merits link prediction presents, they are only enjoyed by large networks. For sparse networks, there is a wide disparity between the links that are likely to form and the ones that include. A barrage of literature has been written to approach this problem; however, they mostly come from the angle of unsupervised learning (UL). While it may seem appropriate based on a dataset’s nature, it does not provide accurate information for sparse networks. Supervised learning could seem reasonable in such cases. This research is aimed at finding the most appropriate link-based link prediction methods in the context of big data based on supervised learning. There is a tone of books written on the same; nonetheless, they are core issues that are not always addressed in these studies, which are critical in understanding the concept of link prediction. This research explicitly looks at the new problems and uses the supervised approach in analyzing them to devise a full-fledge holistic link-based link prediction method. Specifically, the network issues that we will be delving into the lack of specificity in the existing techniques, observational periods, variance reduction, sampling approaches, and topological causes of imbalances. In the subsequent sections of the paper, we explain the theory prediction algorithms, precisely the flow-based process. We specifically address the problems on sparse networks that are never discussed with other prediction methods. The resolutions made by addressing the above techniques place our framework above the previous literature’s unsupervised approaches.

scholarly journals Benchmarking Nonstationary Time Series Prediction

2021 ◽  
Author(s):  
Rebecca Pontes Salles ◽  
Eduardo Ogasawara ◽  
Pedro González
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Data Transformation ◽  
Nonstationary Time Series ◽  
Prediction Methods ◽  
Simple Task ◽  
Model Training ◽  
Transformation Methods

The prediction of time series has gained increasingly more attention among researchers since it is a crucial aspect of decision-making activities. Unfortunately, most time series prediction methods assume the property of stationarity, i.e., statistical properties do not change over time. In practice, it is the exception and not the rule in most real datasets. Several transformation methods were designed to treat nonstationarity in time series. In this context, nonstationary time series prediction is challenging since it demands knowledge of both data transformation and prediction methods. Since there are no silver bullets, it leads to exploring a large number of data transformation and prediction method combinations for building prediction setups. However, selecting a prediction setup that is appropriate to a particular time series and application is not a simple task. Benchmarking of different candidate combinations helps this selection. This work contributes by providing a review and experimental analysis of transformation methods and a systematic framework (TSPred) for benchmarking and selecting prediction setups for nonstationary time series. Suitable nonstationary time series transformation methods provided improvements of more than 30% in prediction accuracy for half of the evaluated time series. They improved the prediction by more than 95% for 10% of the time series. The features provided by TSPred are also shown to be competitive regarding prediction accuracy. Furthermore, the adoption of a validation phase during model training enables the selection of suitable transformation methods.

scholarly journals Seven-Layer Model in Complex Networks Link Prediction: A Survey

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6560
Author(s):  
Hui Wang ◽  
Zichun Le
Keyword(s):  
Complex Networks ◽  
Supervised Learning ◽  
Link Prediction ◽  
Complex Analysis ◽  
Auxiliary Information ◽  
Prediction Method ◽  
Prediction Methods ◽  
Layer Model ◽  
The Future ◽  
High Level

Link prediction is the most basic and essential problem in complex networks. This study analyzes the observed topological, time, attributive, label, weight, directional, and symbolic features and auxiliary information to find the lack of connection and predict the future possible connection. For discussion and analysis of the evolution of the network, the network model is of great significance. In the past two decades, link prediction has attracted extensive attention from experts in various fields, who have published numerous high-level papers, but few combine interdisciplinary characteristics. This survey analyzes and discusses the existing link prediction methods. The idea of stratification is introduced into the classification system of link prediction for the first time and proposes the design idea of a seven-layer model, namely the network, metadata, feature classification, selection input, processing, selection, and output layers. Among them, the processing layer divides link prediction methods into similarity-based, probabilistic, likelihood, supervised learning, semi-supervised learning, unsupervised learning, and reinforcement learning methods. The input features, evaluation metrics, complex analysis, experimental comparisons, relative merits, common dataset and open-source implementations for each link prediction method are then discussed in detail. Through analysis and comparison, we found that the link prediction method based on graph structure features has better prediction performance. Finally, the future development direction of link prediction in complex networks is discussed.

scholarly journals Hybrid Prediction Method for ECG Signals Based on VMD, PSR, and RBF Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fuying Huang ◽  
Tuanfa Qin ◽  
Limei Wang ◽  
Haibin Wan
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Rbf Neural Network ◽  
Prediction Method ◽  
Prediction Methods ◽  
Ecg Signal ◽  
Intrinsic Mode Functions ◽  
Hybrid Prediction ◽  
Ecg Signals ◽  
Input Parameters

To explore a method to predict ECG signals in body area networks (BANs), we propose a hybrid prediction method for ECG signals in this paper. The proposed method combines variational mode decomposition (VMD), phase space reconstruction (PSR), and a radial basis function (RBF) neural network to predict an ECG signal. To reduce the nonstationarity and randomness of the ECG signal, we use VMD to decompose the ECG signal into several intrinsic mode functions (IMFs) with finite bandwidth, which is helpful to improve the prediction accuracy. The input parameters of the RBF neural network affect the prediction accuracy and computational burden. We employ PSR to optimize input parameters of the RBF neural network. To evaluate the prediction performance of the proposed method, we carry out many simulation experiments on ECG data from the MIT-BIH Arrhythmia Database. The experimental results show that the root mean square error (RMSE) and mean absolute error (MAE) of the proposed method are of 10-3 magnitude, while the RMSE and MAE of some competitive prediction methods are of 10-2 magnitude. Compared with other several prediction methods, our method obviously improves the prediction accuracy of ECG signals.

scholarly journals Comparação de Técnicas de Predição de Links em Sub-redes de Coautoria Formada por Currículos da Plataforma Lattes

2017 ◽  
Author(s):  
Douglas V. Santos ◽  
Thiago C. Cunha ◽  
Antônio B. O. Silva ◽  
Fernando S. Parreiras ◽  
Orlando A. Gomes
Keyword(s):  
Link Prediction ◽  
Prediction Accuracy ◽  
Prediction Methods ◽  
Spectral Evolution ◽  
Technology And Innovation ◽  
Coauthorship Network ◽  
Science Technology ◽  
Science Technology And Innovation ◽  
Over Time ◽  
Better Than

The study of Lattes platform allows addressing and analyzing Brazil researchers network which could be useful for defining politics to improve science, technology, and innovation. This work evaluated Lattes Platform coauthorship network. This network evolves over time, which means that new coauthorships will arise in future. Therefore, using link prediction methods in this network would help to identify growing knowledge areas in Brazil. The used technics were Spectral Evolution, wich is new in this context, Common Neighbors, Adamic-Adar and Jaccard. The main goal was to evaluate the link prediction accuracy with different methods at the coauthorship network of Lattes Platform. The Spectral Evolution was worse than the others. Adamic-Adar method presented the best result - 817 times better than the random link prediction.

scholarly journals Link Prediction Methods and Their Accuracy for Different Social Networks and Network Metrics

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Fei Gao ◽  
Katarzyna Musial ◽  
Colin Cooper ◽  
Sophia Tsoka
Keyword(s):  
Social Networks ◽  
Link Prediction ◽  
Prediction Method ◽  
Prediction Methods ◽  
Online Systems ◽  
Network Characteristics ◽  
High Prediction ◽  
The Right ◽  
Accuracy Of Prediction ◽  
Network Metrics

Currently, we are experiencing a rapid growth of the number of social-based online systems. The availability of the vast amounts of data gathered in those systems brings new challenges that we face when trying to analyse it. One of the intensively researched topics is theprediction of social connections between users. Although a lot of effort has been made to develop new prediction approaches, the existing methods are not comprehensively analysed. In this paper we investigate the correlation between network metrics and accuracy of different prediction methods. We selected six time-stamped real-world social networks and ten most widely used link prediction methods. The results of the experiments show that the performance of some methods has a strong correlation with certain network metrics. We managed to distinguish “prediction friendly” networks, for which most of the prediction methods give good performance, as well as “prediction unfriendly” networks, for which most of the methods result in high prediction error. Correlation analysis between network metrics and prediction accuracy of prediction methods may form the basis of a metalearning system where based on network characteristics it will be able to recommend the right prediction method for a given network.

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