Aiming at the difficulty of quantitatively evaluating the critical processes in the manufacturing process of complex mechanical products, a critical process identification method based on the pagerank algorithm is pro...
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Aiming at the difficulty of quantitatively evaluating the critical processes in the manufacturing process of complex mechanical products, a critical process identification method based on the pagerank algorithm is proposed with the goal of identifying key processes in the machining process. Based on the complex network theory, the error transfer network model of the machining process is established in this paper. Adopting the actual machining process as the data set of the complex network, the weights of the machining feature nodes are calculated by the pagerank ranking algorithm, and the nodes are ranked according to the weight values to assess the influence and importance of the nodes in the network model. Finally, taking the connecting rod machining process of a medium-speed marine diesel engine as an example, the results show that the method can quickly and effectively identify the key processes in the machining process.
The rapid growth of the amount of information on the Internet makes web search technology become the main means for people to obtain information. How to measure the importance of web pages efficiently and quickly has ...
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ISBN:
(数字)9781510651890
ISBN:
(纸本)9781510651890;9781510651883
The rapid growth of the amount of information on the Internet makes web search technology become the main means for people to obtain information. How to measure the importance of web pages efficiently and quickly has become an important topic, and has different degrees of influence on all aspects of the web page, such as web crawling, web page grading and ranking. This paper discusses an important algorithm-pagerank, based on Markov chain model then a discussion of the advantages and disadvantages of pagerank is provided here. Finally, according to the drawbacks, some corrections and improvements are also discussed in this paper.
Consumer reviews play a crucial role in evaluating products on online e-commerce platforms. Unlike numerical ratings, online reviews provide valuable information and sentiment. However, existing studies often overlook...
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Consumer reviews play a crucial role in evaluating products on online e-commerce platforms. Unlike numerical ratings, online reviews provide valuable information and sentiment. However, existing studies often overlook the unique interrelationships between products on e-commerce platforms, and fail to adequately capture the psychological behavior of consumers during online shopping. To address these gaps, this study presents a novel product recommendation model based on online reviews that evaluates products' multi-attribute performances. The study first identifies the product attributes that are most important to consumers by analyzing review texts. Then, this study calculates the attribute performance scores of each product by considering consumer sentiment and the usefulness of online reviews. Next, it identifies competitors for the target product using a weighted Euclidean distance function and ranks all products employing an improved pagerank algorithm. Finally, to illustrate the validity of the proposed model, the study conducts a case study using a dataset of 41,352 online reviews obtained from Best Buy, and segments the data into three categories according to price. Comparative results with traditional MCDM models show that among the three categories, our results achieved a maximum improvement of 18.3% in the Spearman correlation coefficient.
pagerank algorithm is a benchmark for many graph analytics and is the underlying kernel for link predictions. recommendation systems. It is an iterative algorithm that updates ranks of pages until the value converges....
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ISBN:
(纸本)9781665414555
pagerank algorithm is a benchmark for many graph analytics and is the underlying kernel for link predictions. recommendation systems. It is an iterative algorithm that updates ranks of pages until the value converges. Implementation of pagerank algorithm on a shared memory architecture while taking advantage of line-grained parallelism using large-scale graphs is a challenging task. In this paper. We present parallel algorithms for computing the pagerank suitable to the shared memory systems. Initially, we present parallel implementations of page-rank algorithms using harrier and lock variants. Later. we propose new approaches which are lock-free and are harrier-less synchronization to overcome the issues of lock based methods. A detailed experimental analysis of our approach is carried out using real-world web graphs from SNAP and Synthetic Graphs from RMAT on an Intel(R) Xeon E5-2660 v4 processor architecture with 56 threads using the POSIX thread library.
The process of ranking scientific publications in dynamic citation networks plays a crucial rule in a variety of applications. Despite the availability of a number of ranking algorithms, most of them use common popula...
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The process of ranking scientific publications in dynamic citation networks plays a crucial rule in a variety of applications. Despite the availability of a number of ranking algorithms, most of them use common popularity metrics such as the citation count, h-index, and Impact Factor (IF). These adopted metrics cause a problem of bias in favor of older publications that took enough time to collect as many citations as possible. This paper focuses on solving the problem of bias by proposing a new ranking algorithm based on the pagerank (PR) algorithm;it is one of the main page ranking algorithms being widely used. The developed algorithm considers a newly suggested metric called the Citation Average rate of Change (CAC). Time information such as publication date and the citation occurrence’s time are used along with citation data to calculate the new metric. The proposed ranking algorithm was tested on a dataset of scientific papers in the field of medical physics published in the Dimensions database from years 2005 to 2017. The experimental results have shown that the proposed ranking algorithm outperforms the pagerank algorithm in ranking scientific publications where 26 papers instead of only 14 were ranked among the top 100 papers of this dataset. In addition, there were no radical changes or unreasonable jump in the ranking process, i.e., the correlation rate between the results of the proposed ranking method and the original pagerank algorithm was 92% based on the Spearman correlation coefficient.
Node importance ranking is one of key problems in the study of complex *** classical pagerank algorithm only focus on the network structure,which lead to inaccurate ranking *** introducing the features of node attribu...
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Node importance ranking is one of key problems in the study of complex *** classical pagerank algorithm only focus on the network structure,which lead to inaccurate ranking *** introducing the features of node attributes and user preference,a novel feature-based pagerank(FBPR) algorithm is proposed to identify the important nodes accurately and *** weight matrix and the fixed teleportation vector are redesigned by the feature similarities in the FBPR *** different application scenarios,we can get different ranking results by adjusting the node attributes factor and the user preference ***,several simulation experiments are presented to verify the effectiveness of the FBPR algorithm.
Network pharmacology is a method to study the mechanism of a Traditional Chinese Medicine (TCM) prescription on a disease. However, most articles using network pharmacology to study the mechanism did not combine the w...
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On-orbit processors generally require reinforcement due to the harsh space radiation environment. Although the traditional full triple modular redundancy (TMR) method can effectively reinforce circuits, it requires ex...
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On-orbit processors generally require reinforcement due to the harsh space radiation environment. Although the traditional full triple modular redundancy (TMR) method can effectively reinforce circuits, it requires excessive physical footprints and energy consumptions. To address this problem, this Letter proposes a general partial TMR method that can effectively reinforce a circuit. The method is based on the pagerank algorithm for sorting the importance of the triggers in a circuit, and the mean time between failure based on the dual logic cone model is used as the criterion to determine triggers that require redundancy under the partial TMR method. The arithmetic module of an optical image processing platform is tested to verify the effectiveness of the partial TMR method proposed in this Letter.
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