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检索条件"主题词=Pagerank Algorithm"

共 158 条记录，以下是121-130 订阅

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Product Rank Based Search Engine for E-Commerce 3

Product Rank Based Search Engine for E-Commerce

引用

3rd International Conference for Convergence in Technology (I2CT)

作者： Rathod, Urvesh Pavate, Aruna Patil, Vaibhav Atharva Coll Engn Comp Engn Mumbai Maharashtra India

ISBN: (纸本)9781538642733

In today's world, e-commerce is the strongest and the fastest growing platform. People inclined to buy products from websites now and then. Up to the present time price comparisons of the same product on different websites are available but now, moving a step ahead, our proposed system tends to serve as a smart web spider for all e-commerce websites, which crawls the whole website for you and find out the cheapest yet best quality products of your choice in real time. Moreover, this assures the union of all e-commerce website in form of one rigorously filtered system. It is also capable of making good recommendations as per customer's requirement and serves as an active agent in the system. This product can also prove beneficial for e-commerce companies because it will be able to track down dead pages, Seller's wrongly entered price or any kind of glitches/errors/fraudulent activities on their websites in real time.

关键词： Web Search Engine Web Crawlers pagerank algorithm Product Rank algorithm

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Java single vs. platform vs. virtual threads runtime performance assessment in the context of key class detection 18

Java single vs. platform vs. virtual threads runtime perform...

引用

18th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI)

作者： Chirila, Ciprian-Bogdan Sora, Ioana Univ Politehn Timisoara Comp & Informat Technol Dept Timisoara Romania

ISBN: (纸本)9798350329537;9798350329520

Key classes are considered the most important classes of a software system. They represent the starting point for reengineering or documentation processes. The detection of key classes is considered crucial in the state of the art, many studies are focused on automatic detection based on class graph system representation. Studies show that class attributes computed with algorithms like Hyperlink-Introduced Topic Search (HITS) and pagerank (PR) give the best precision and recall performance values in detecting key classes. The runtime execution of the two algorithms is critical when they run on graphs having different class relationship weights. To ameliorate the time execution problem we experiment with the two algorithm parallel implementations based on Java: i) single thread, ii) platform or operating system threads and iii) virtual threads. The experiments are fulfilled on a set of 14 Java projects. The results show that single thread implementations for project having a relative small number of classes, namely under 1,200, perform better than platform threads implementations. Conversely, virtual threads perform better than any single thread implementation. We conclude that virtual thread model speeds up the computation of attributes with a runtime decrease of 58.41% against the single thread model.

关键词： Java threads Java virtual threads Hits algorithm pagerank algorithm key classes detection

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Construction and Influence Analysis of Co-author Network based on Network Science

Construction and Influence Analysis of Co-author Network bas...

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International Conference on Advanced Nano-Technology and Biomedical Material (ANTBM)

作者： Yang, You-she Zheng, Ming-fa He, Qi-fang Wang, Tie-zhu Air Force Engn Univ Coll Sci Xian 710051 Peoples R China Coll Xijing Dept Math Xian 710236 Peoples R China Air Force Engn Univ Informat & Nav Coll Xian 710077 Peoples R China Air Force Engn Univ Informat Ctr Xian 710051 Peoples R China

ISBN: (纸本)9783038352044

In this paper, we are devoted to the construction and analysis of research network. Firstly, on the basis of data Erdos1, a co-author network is built and two novel measures are proposed to analyze properties of the co-author network. A data extraction method using string matching technique is developed and the network is visualized using UCINET. Then, the first-order and second-order entropy are defined to depict the complexity of the network, and the node's invalidity probability and load-bearing capacity are defined to depict the robustness of the network.

关键词： Network construction Co-author network Importance degree pagerank algorithm

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Word Embedding-Based Biomedical Text Summarization 4th

Word Embedding-Based Biomedical Text Summarization

引用

4th International Conference of Reliable Information and Communication Technology (IRICT)

作者： Rouane, Oussama Belhadef, Hacene Bouakkaz, Mustapha Univ Constantine 2 Abdelhamid Mehri Constantine Algeria Univ Amar Telidgi Comp Sci Dept Fac Sci Laghouat Algeria

ISBN: (纸本)9783030335823;9783030335816

In this paper, we have proposed a novel word embedding-based biomedical text summarizer. Biomedical words are represented by real dense vectors. Sentences are represented by summing-up the word vectors that contain. The pagerank algorithm is applied to rank sentences using the cosine similarity as a distance measure between sentences vectors. The top N highly ranked sentences are selected to build the summary. For the evaluation, we created a corpus of 200 biomedical papers downloaded from the Biomed Central full-text database. We used a pre-trained Word2vec model of word vectors generated from a combination of PubMed, PMC, and recent English Wikipedia dump texts. We compared our method with four other summarizers using: ROUGE-1, ROUGE-2, ROUGE-3, and ROUGE-SU4 metrics by evaluating the generated summaries with the abstracts of papers. Our summarizer achieved an improvement of 3.48%, 7.68%, 9.76%, and 3.47% respectively against the second-ranked summarizer.

关键词： Biomedical text summarization Word embedding Word2vec pagerank algorithm ROUGE metrics

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Identifying Main Topics in Density-based Spatial Clusters using Network-based Representative Document Extraction 8

Identifying Main Topics in Density-based Spatial Clusters us...

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8th IEEE International Workshop on Computational Intelligence and Applications (IWCIA)

作者： Sakai, Tatsuhiro Tamura, Keiichi Kitakami, Hajime Hiroshima City Univ Grad Sch Informat Sci Asa Minami Ku 3-4-1 Ozuka Higashi Hiroshima 7313194 Japan

ISBN: (纸本)9781479998869

Geo-tagged documents on social media are usually related to local topics and events. Extracting areas of interest associated with local "attractive" topics from geo-tagged documents is one of the most important challenges in many application domains. In this paper, we propose a novel method for extracting the areas of interest from geo-tagged documents. There are two main steps in the proposed method. First, the (epsilon, sigma)-density-based adaptive spatial clustering algorithm extracts areas where local topics are attracting attention as spatial clusters. Second, representative geo-tagged documents are detected to identify the main topic in each spatial cluster. The (epsilon, sigma)-density-based adaptive spatial clustering algorithm changes the threshold for seamlessly extracting spatial clusters regardless of the local densities of the posted geo-tagged documents. Moreover, the proposed method utilizes the network-based important sentence extraction method in order to extract representative geo-tagged documents from each spatial cluster. The experimental results show that the proposed method can extract the areas of interest as spatial clusters and representative documents as main topics.

关键词： Density-based spatial clustering pagerank algorithm Node-based betweenness centrality Geo-tagged tweet Local topic extraction

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A Faster Non-Linear Iteration Solver using Graph Computing and its Application in Power Flow Calculation 9th

A Faster Non-Linear Iteration Solver using Graph Computing a...

引用

9th International Conference on Applied Energy (ICAE)

作者： Liu, Kewen Liu, Guangyi Xie, Kai Wang, Zhiwei Global Energy Interconnect Res Inst North Amer San Jose CA 95134 USA Global Energy Interconnect Res Inst Beijing 102209 Peoples R China

To solve large scale fast matrix calculation problems, a Non-Linear Iteration Solver (NLIS) based on a BSP model using a graph database & computing technology is proposed in this paper. A node and edge level parallel iterative algorithm is implemented by using a graph computing algorithm combined with a message passing mechanism. IEEE testing cases and actual operation data of a provincial power system in China are used to verify the method, then expand the MP 10790 to over 100k and 1 million nodes systems for the calculation stress test on a conventional server. (C) 2017 The Authors. Published by Elsevier Ltd.

关键词： Graph theory graph database graph computing power system analysis graph data modeling pagerank algorithm Bulk Synchronous Parallel Computing Model

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Detection of overlapping protein complexes using a protein ranking algorithm 9th

Detection of overlapping protein complexes using a protein r...

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9th Annual International Conference on Innovations in Information Technology (IIT)

作者： Hanna, Eileen Marie United Arab Emirates Univ Coll Informat Technol Al Ain U Arab Emirates

ISBN: (纸本)9781467362030

The detection of protein complexes is evidently a cornerstone of understanding various biological processes and identifying key genes causing different diseases. Accordingly, many methods aiming at detecting protein complexes were developed. Recently, a novel method called ProRank was introduced. This method uses a ranking algorithm to detect protein complexes by ordering proteins based on their importance in the interaction network and by accounting for the evolutionary relationships among them. The experimental results showed that ProRank outperformed several well-known methods in terms of the number of detected complexes with high accuracy, precision and recall levels. In this paper, we overcome a drawback of the ProRank algorithm and further improve its performance by allowing detected protein complexes to overlap;a supposition that was not considered in the original version of the method.

关键词： overlapping protein complexes protein-protein interaction pagerank algorithm essential protein

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Evolution Analysis of Online Knowledge Transfer Network

Evolution Analysis of Online Knowledge Transfer Network

引用

4th International Conference on Information Computing and Applications (ICICA)

作者： Wei, Jing Song, Ruixiao Miao, Jianjun Xu, Zan Nanjing Univ Post & Telecommun Coll Econ & Management Nanjing 210046 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 210016 Peoples R China

ISBN: (纸本)9783642539329;9783642539312

Based on the study of online knowledge transfer network`s topology and dynamic statistical characteristics, this paper found the characteristic differences between the evolution of online knowledge transfer network and the traditional BA network model. Through the empirical data analysis of a BBS forum, it gives out an evolution model of the online knowledge transfer network based on the pagerank algorithm. Meanwhile, in the network analysis it also found that the network growth model has a power-law distribution of degree. Through the control of the attenuation coefficient and the node degree growth factor, it can ultimately realize growth control of the online knowledge transfer network.

关键词： knowledge management knowledge transfer complex network BBS pagerank algorithm

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Research on paper influence evaluation based on citation network 4

Research on paper influence evaluation based on citation net...

引用

4th International Conference on Machine Learning, Big Data and Business Intelligence, MLBDBI 2022

作者： Liu, Kaiyu Kan, Yujin Liu, Jinmei Wang, Xuan Jincheng College of Nanjing University of Aeronautics and Astronautics China

ISBN: (纸本)9798350333947

In order to better discover high-quality papers, this paper takes the literature in the field of computer vision as the research object to construct a citation network. Aiming at the problems of unreasonable distribution of document weights and high scores of old documents when using the pagerank algorithm to calculate the importance of papers, this paper improves the pagerank algorithm, considering the publication time, citation time and the impact factor of the publication journal, and proposes a new method. A new weighted pagerank algorithm. The experimental results show that the algorithm can better mine high-impact papers from the citation network. © 2022 IEEE.

关键词： Citation network pagerank algorithm paper influence

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AI Driven e - Commerce Product Recommendation 6

AI Driven e - Commerce Product Recommendation

引用

6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Sinha, Shambhavi Rakhra, Manik Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798350304480

E-commerce is growing in today's world, and if you want to sell more to your customers and stay ahead of the competition, you need to keep up with online shopping trends and know what they want. In the past few years, the number of products available on shopping websites has grown by a huge amount. This leads to the problem of systems growing. Websites use a lot of data mining tools and suggestion systems, but users still might not get the best product for their search, which hurts the customer experience. Getting the search results as small as possible and combining goods from different websites can save time and improve the customer experience. Also, no shopping website has a way for customers to track prices and make predictions about them. With the help of web scraping and the Product Rank algorithm, our system tends to give customers rich and expensive goods from all shopping sites at the cheapest price of their choice without having to use human filters. With sales going up and income going up, we must also show how much we lost when we bargained with customers. Our proposed system tends to work like a smart web spider for all e-commerce sites. It scans every page for you and finds the most affordable, ideal-quality goods of your choice in actual time. This also makes sure that all e-commerce websites will be merged into a single, highly screened system. It can also make good suggestions based on what the customer wants and act as an involved player in the system. This tool can also help e-commerce companies because it can find dead pages, prices that the seller put in wrong, glitches, mistakes, and fraudulent activities on their websites in real time. © 2023 IEEE.

关键词： pagerank algorithm Product Rank algorithm Web Crawlers Web Search Engine

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