CINXE.COM

<html><head><title>EAI Endorsed Transactions on Scalable Information Systems - EUDL</title><link rel="icon" href="/images/favicon.ico"><link rel="stylesheet" type="text/css" href="/css/screen.css"><link rel="stylesheet" href="/css/zenburn.css"><meta http-equiv="Content-Type" content="charset=utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="Description" content="Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/sis/index"><script type="text/javascript" src="https://services.eai.eu//load-signup-form/EAI"></script><script type="text/javascript" src="https://services.eai.eu//ujs/forms/signup/sso-client.js"></script><script type="text/javascript">if (!window.EUDL){ window.EUDL={} };EUDL.cas_url="https://account.eai.eu/cas";EUDL.profile_url="https://account.eai.eu";if(window.SSO){SSO.set_mode('eai')};</script><script type="text/javascript" src="/js/jquery.js"></script><script type="text/javascript" src="/js/jquery.cookie.js"></script><script type="text/javascript" src="/js/sso.js"></script><script type="text/javascript" src="/js/jscal2.js"></script><script type="text/javascript" src="/js/lang/en.js"></script><script type="text/javascript" src="/js/jquery.colorbox-min.js"></script><script type="text/javascript" src="/js/eudl.js"></script><script type="text/javascript" src="/js/journal.js"></script><script type="text/javascript" src="/js/tabs-journal.js"></script><link rel="stylesheet" type="text/css" href="/css/jscal/jscal2.css"><link rel="stylesheet" type="text/css" href="/css/jscal/eudl/eudl.css"><link rel="stylesheet" type="text/css" href="/css/colorbox.css"></head><body><div id="eudl-page-head"><div id="eudl-page-header"><section id="user-area"><div><nav id="right-nav"><a href="/about">About</a> | <a href="/contact">Contact Us</a> | <a class="register" href="https://account.eai.eu/register?service=http%3A%2F%2Feudl.eu%2Fjournal%2Fsis">Register</a> | <a class="login" href="https://account.eai.eu/cas/login?service=http%3A%2F%2Feudl.eu%2Fjournal%2Fsis">Login</a></nav></div></section></div></div><div id="eudl-page"><header><section id="topbar-ads"><div><a href="https://eudl.eu/"><img class="eudl-logo-top" src="https://eudl.eu/images/eudl-logo.png"></a><a href="https://eai.eu/eai-community/?mtm_campaign=community_membership&mtm_kwd=eudl_community&mtm_source=eudl&mtm_medium=eudl_banner"><img class="eudl-ads-top" src="https://eudl.eu/images/upbanner.png"></a></div></section><section id="menu"><nav><a href="/proceedings" class=""><span>Proceedings</span><span class="icon"></span></a><a href="/series" class=""><span>Series</span><span class="icon"></span></a><a href="/journals" class="current"><span>Journals</span><span class="icon"></span></a><a href="/content" class=""><span>Search</span><span class="icon"></span></a><a href="http://eai.eu/">EAI</a></nav></section></header><div id="eaientran"></div><section id="content"><section id="journal"><form class="search-form" id="article_search" method="get"><section class="cover-and-filters"><section class="cover"><img src="/attachment/63349"></section><section class="issn"><strong>ISSN: </strong>2032-9407</section><section class="subscribe link"><a href="/journal/sis/subscribe">Subscribe</a></section><section class="escripts link"><a href="https://escripts.eai.eu/paper/submit">Submit Article</a></section><section class="instructions link"><a href="/instructions">Submission Instructions</a></section><section class="openaccess link"><a href="/openaccess">Open Access Information</a></section><section class="ethics link"><a href="/ethics">Ethics and Malpractice Statement</a></section><section class="most-recent link"><a href="/issue/sis/11/6">Most Recent Issue</a></section><section class="browse-filters"><div class="browse-by"><a class="browse-link">2024<span class="pointer"></span></a><div class="filters"><a href="/issue/sis/11/6" class="filter">Issue 6</a><a href="/issue/sis/11/5" class="filter">Issue 5</a><a href="/issue/sis/11/4" class="filter">Issue 4</a><a href="/issue/sis/11/3" class="filter">Issue 3</a><a href="/issue/sis/11/2" class="filter">Issue 2</a><a href="/issue/sis/11/1" class="filter">Issue 1</a></div><a class="browse-link">2023<span class="pointer"></span></a><div class="filters"><a href="/issue/sis/10/6" class="filter">Issue 6</a><a href="/issue/sis/10/5" class="filter">Issue 5</a><a href="/issue/sis/10/4" class="filter">Issue 4</a><a href="/issue/sis/10/3" class="filter">Issue 3</a><a href="/issue/sis/10/2" class="filter">Issue 2</a><a href="/issue/sis/10/1" class="filter">Issue 1</a></div><a class="browse-link">2022<span class="pointer"></span></a><div class="filters"><a href="/issue/sis/9/6" class="filter">Issue 6</a><a href="/issue/sis/9/5" class="filter">Issue 5</a><a href="/issue/sis/9/4" class="filter">Issue 4</a><a href="/issue/sis/9/36" class="filter">Issue 36</a><a href="/issue/sis/9/35" class="filter">Issue 35</a><a href="/issue/sis/9/34" class="filter">Issue 34</a></div><a class="browse-link">2021<span class="pointer"></span></a><div class="filters"><a href="/issue/sis/8/33" class="filter">Issue 33</a><a href="/issue/sis/8/32" class="filter">Issue 32</a><a href="/issue/sis/8/31" class="filter">Issue 31</a><a href="/issue/sis/8/30" class="filter">Issue 30</a><a href="/issue/sis/8/29" class="filter">Issue 29</a></div><a class="browse-link">2020<span class="pointer"></span></a><div class="filters"><a href="/issue/sis/7/28" class="filter">Issue 28</a><a href="/issue/sis/7/27" class="filter">Issue 27</a><a href="/issue/sis/7/26" class="filter">Issue 26</a><a href="/issue/sis/7/25" class="filter">Issue 25</a><a href="/issue/sis/7/24" class="filter">Issue 24</a></div><a class="browse-link">2019<span class="pointer"></span></a><div class="filters"><a href="/issue/sis/6/23" class="filter">Issue 23</a><a href="/issue/sis/6/22" class="filter">Issue 22</a><a href="/issue/sis/6/21" class="filter">Issue 21</a><a href="/issue/sis/6/20" class="filter">Issue 20</a></div><a class="browse-link">2018<span class="pointer"></span></a><div class="filters"><a href="/issue/sis/5/19" class="filter">Issue 19</a><a href="/issue/sis/5/18" class="filter">Issue 18</a><a href="/issue/sis/5/17" class="filter">Issue 17</a><a href="/issue/sis/5/16" class="filter">Issue 16</a></div><a class="browse-link">2017<span class="pointer"></span></a><div class="filters"><a href="/issue/sis/4/15" class="filter">Issue 15</a><a href="/issue/sis/4/14" class="filter">Issue 14</a><a href="/issue/sis/4/13" class="filter">Issue 13</a><a href="/issue/sis/4/12" class="filter">Issue 12</a></div><a class="browse-link">2016<span class="pointer"></span></a><div class="filters"><a href="/issue/sis/3/11" class="filter">Issue 11</a><a href="/issue/sis/3/10" class="filter">Issue 10</a><a href="/issue/sis/3/9" class="filter">Issue 9</a><a href="/issue/sis/3/8" class="filter">Issue 8</a></div><a class="browse-link">2015<span class="pointer"></span></a><div class="filters"><a href="/issue/sis/2/7" class="filter">Issue 7</a><a href="/issue/sis/2/6" class="filter">Issue 6</a><a href="/issue/sis/2/5" class="filter">Issue 5</a><a href="/issue/sis/2/4" class="filter">Issue 4</a></div><a class="browse-link">2014<span class="pointer"></span></a><div class="filters"><a href="/issue/sis/1/3" class="filter">Issue 3</a><a href="/issue/sis/1/2" class="filter">Issue 2</a><a href="/issue/sis/1/1" class="filter">Issue 1</a></div></div></section><section class="cover"><a href="https://eai.eu/eai-community/?mtm_campaign=community_membership&mtm_kwd=eudl_bids&mtm_source=eudl&mtm_medium=eudl_banner"><img src="https://eudl.eu/images/banner-outside2.png"></a></section></section><section class="info-and-search"><div class="manage-menu"><h2 class="blurb">Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/sis/index</h2><section class="editors"><strong>Editor(s)-in-Chief: </strong><span class="editor">Hua Wang</span> and <span class="editor">Xiaohua Jia</span></section><section class="meta-tabs"><div class="tabs"><ul><li><a name="aims-and-scope">Aims & Scope</a></li><li><a name="Indexing">Indexing</a></li><li><a name="EditorialBoard">Editorial Board</a></li><li><a name="SpecialIssues">Special Issues</a></li></ul></div><div class="content"><div name="aims-and-scope"><div class="abstract"><p>EAI Endorsed Transactions on Scalable Information Systems is open access, a peer-reviewed scholarly journal focused on scalable distributed information systems, scalable, data mining, grid information systems, and more. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications. From 2024, the journal started to publish twelve issues per year. Authors are not charged for article submission and processing.</p> <p>INDEXING: ESCI-WoS (IF: 1.3), Compendex, DOAJ, ProQuest, EBSCO</p> <p>The scope of the journal includes:</p> <ul> <li>Scalable distributed information systems</li> <li>Scalable grid information systems</li> <li>Parallel information processing and systems</li> <li>Web information searching and retrieval</li> <li>Data mining</li> <li>Content delivery networks (CDN)</li> <li>VLDB</li> <li>P2P systems</li> <li>Scalable mobile and wireless database systems</li> <li>Large scale sensor network systems</li> <li>Index compression methods</li> <li>Architectures for scalability</li> <li>Scalable information system applications</li> <li>Evaluation metrics for scalability</li> <li>Information security</li> </ul></div></div><div name="Indexing"><div class="abstract"><ul> <li><a href="https://mjl.clarivate.com/home">Web of Science Core Collection</a></li> <li><a href="https://www.engineeringvillage.com/home.url">Ei Compendex</a></li> <li><a href="https://doaj.org/toc/2032-9407">DOAJ</a></li> <li><a href="https://search.crossref.org/?q=2032-9407">CrossRef</a></li> <li><a href="https://www.ebsco.com/products/ebsco-discovery-service">EBSCO Discovery Service</a></li> <li><a href="https://www.worldcat.org/title/eai-endorsed-transactions-on-scalable-information-systems/oclc/913714002&referer=brief_results">OCLC Discovery Services</a></li> <li><a href="https://europub.co.uk/journals/8124">EuroPub</a></li> <li><a href="http://miar.ub.edu/issn/2032-9407">MIAR</a></li> <li><a href="https://rzblx1.uni-regensburg.de/ezeit/detail.phtml?bibid=AAAAA&colors=7&lang=de&jour_id=237211">Elektronische Zeitschriftenbibliothek</a></li> <li><a href="https://publons.com/journal/37157/icst-transactions-on-scalable-information-systems">Publons</a></li> <li><a href="http://ulrichsweb.serialssolutions.com/login">UlrichsWEB</a></li> <li><a href="https://www.heal-link.gr/en/home-2/">Hellenic Academic Libraries Link</a></li> <li><a href="https://www.ingentaconnect.com/content/doaj/20329407">Ingenta Connect</a></li> <li><a href="https://www.proquest.com/products-services/Publicly-Available-Content-Database.html#overviewlinkSection">Publicly Available Content Database (ProQuest)</a></li> <li><a href="https://www.proquest.com/products-services/adv_tech_aero.html">Advanced Technologies & Aerospace Database (ProQuest)</a></li> <li><a href="https://www.proquest.com/products-services/databases/pq_scitech.html">SciTech Premium Collection (ProQuest)</a></li> <li><a href="https://scholar.google.sk/scholar?start=0&q=source:eai+source:endorsed+source:transactions+source:on+source:scalable+source:information+source:systems&hl=es&as_sdt=0,5&as_ylo=2018">Google Scholar</a></li> </ul></div></div><div name="SpecialIssues"><div class="abstract"><p><em>Call for Papers:</em> <a href="https://escripts.eai.eu/publication/366">Special issue on: Real-time image information processing with deep neural networks and data mining technologies</a> (Manuscript submission deadline: 2022-02-28; Notification of acceptance: 2022-04-15; Submission of final revised paper: 2022-05-15; Publication of special issue (tentative): 2022-06-15)</p> <p><em>Guest Editor:</em> Dr. Prof. Hang Li (Northeastern University, China) <em>Guest Editor:</em> Dr. Prof. Jochen Schiewe (HafenCity Universität Hamburg, Germany)</p></div></div><div name="EditorialBoard"><div class="abstract"><ul> <li>Editors-in-Chief</li> <li>Hua Wang, Victoria University, Australia</li> <li>Xiaohua Jia, City University of Hong Kong</li> <li>Editorial board</li> <li>Manik Sharma, DAV University, India</li> <li>Ajay Kattepur (Tata Consultancy Services)</li> <li>Aniello Castiglione (University of Salerno)</li> <li>Chang Choi (Chosun University)</li> <li>Cho-Li Wang (University of Hong Kong)</li> <li>Daniel S. Katz (University of Chicago)</li> <li>Fabrizio Silvestri (ISTI – CNR, Italy)</li> <li>Hamed Taherdoost (Hamta Business Solution Snd)</li> <li>Heng Tao Shen (University of Queensland)</li> <li>Houbing Song (Embry-Riddle Aeronautical University)</li> <li>José Manuel Machado (University of Minho, Portugal)</li> <li>Jose Merseguer (Universidad de Zaragoza)</li> <li>Jie Li (University of Tsukuba)</li> <li>Lin Yun (Harbin Engineering University)</li> <li>Phan Cong Vinh (Nguyen Tat Thanh University)</li> <li>Raj Gururajan (University of Southern Queensland)</li> <li>Sherman Chow (Chinese University of Hong Kong)</li> <li>Silva Fábio (University of Minho, Portugal)</li> <li>Steve Beitzel (Telcordia)</li> <li>Tzung-Pei Hong (National University of Kaohsiung, Kaohsing City, Taiwan)</li> <li>Wang-Chien Lee (The Pennsylvania State University)</li> <li>Weili Wu (The University of Texas at Dallas)</li> <li>Xueyan Tang (Nanyang Technological University)</li> <li>Vijayakumar Ponnusamy (SRM University, India)</li> <li>J Amudhavel (KL University, India)</li> <li>Yingshu Li (Georgia State University)</li> <li>Jerry Chun-Wei Lin (Western Norway University of Applied Sciences, Norway)</li> <li>Karolj Skala (Ruđer Bošković Institute, Croatia)</li> <li>Xiao-Zhi Gao (University of Eastern Finland, Finland)</li> <li>Thaier Hayajneh (Fordham University, USA)</li> <li>Chin-Ling Chen (Chaoyang University of Technology, Taiwan)</li> <li>Nuno M. Garcia (Faculty of Sciences, University of Lisbon, Portugal)</li> <li>Arianna D'Ulizia (Consiglio Nazionale delle Ricerche (CNR), Italy)</li> <li>Robertas Damaševičius (Kaunas University of Technology (KTU), Lithuania)</li> <li>Hiep Xuan Huynh (Can Tho University, VietNam)</li> <li>Ji Zhang (University of Southern Queensland, Australia)</li> <li>Xiaohui Tao (University of Southern Queensland, Australia)</li> <li>Ye Wang (National University of Defense Technology, China)</li> <li>Nageswara Rao Moparthi (KL University, India)</li> <li>Shuai Liu (Hunan Normal University, China)</li> <li>Prof Xiaoming Fu (Georg-August-University of Goettingen, Germany)</li> <li>Prof Zhisheng Huang (Vrije University of Amsterdam)</li> <li>Prof Rose Quan (Northumbria University, UK)</li> <li>Prof Shi Dong (Zhoukou Normal University, China)</li> <li>Dr Limei Peng (Kyungpook National University, South Korea)</li> <li>Prof Hui Ma( Victoria University of Wellington, New Zealand)</li> <li>Dr. Venkatesan Subramanian (Indian Institute of Information Technology – Allahabad, India)</li> <li>Dr Pon Harshavardhanan (VIT Bhopal University, India)</li> <li>Dr. Manish Kumar (The Indian Institute of Information Technology, Allahabad, India)</li> <li>Muzammil Hussain, University of Management and Technology, Lahore, Pakistan</li> <li>Michael Bewong, Charles Sturt University, Australia</li> <li>Shabir Ahmad, Gachon University, Korea</li> <li>Vu Nguyen, University of Science, Vietnam</li> <li>Xiaodi Huang, Charles Sturt University, Australia</li> <li>Jianming Yong, University of Southern Queensland, Australia</li> <li>Yogeshwar Vijayakumar Navandar; National Institute of Technology, Indian.</li> <li>Zhengyi Chai, Tiangong University in China, China</li> <li>Chuanlong Wang, Taiyuan Normal University, China</li> <li>Chin-Feng Lee, Chaoyang University of Technology, Taiwan</li> <li>Hsing-Chung Chen (Jack Chen), Asia University, Taiwan</li> <li>Wen-Yang Lin, National University of Kaohsiung, Taiwan</li> <li>Chun-Hao Chen, National Kaohsiung University of Science and Technology, Taiwan</li> <li>Mudasir Mohd, University of Kashmir, India.</li> <li>BalaAnand Muthu, INTI International University, Malaysia.</li> <li>Md Rafiqul Islam, Australian Institute of Higher Education, Australia.</li> <li>Jin Wang, Institute of Applied Physics and Computational Mathematics, China.</li> <li>Chandu Thota, University of Nicosia, Cyprus.</li> <li>Haris M. Khalid, University of Dubai, UAE.</li> <li>Dr. G. Reza Nasiri, Alzahra University, Tehran, Iran.</li> <li>Siuly Siuly, Victoria University, Australia</li> <li>Bishnu Prasad Gautam, Kanazawa Gakuin University, Japan</li> <li>Sivaparthipan C B, Bharathiar University, India</li> <li>Ting-Chia Hsu, National Taiwan Normal University, Taiwan</li> <li>Punitha Palanisamy, Tagore IET, India</li> <li>Lakshmana Kumar R, Tagore IET, India</li> <li>Weiwei Jiang, Beijing University of Posts and Telecommunications, Taiwan</li> </ul></div></div></div></section><br><section class="article-tabs"><div class="tabs"><ul><li><a name="recent" onclick="handleRecentClick()">Recently Published</a></li><li><a name="popular" onclick="handlePopularClick()">Most Popular</a></li></ul></div><div class="contents"><div class="expandable-list"><ul class="results-list"><li class="result-item article-light recent"><h3><a href="../doi/10.4108/eetsis.4435">Explainable Neural Network analysis on Movie Success Prediction</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis 24(4): </dd><br><dt class="title">Authors: </dt><dd class="value">Sagar Dhanraj Pande, S Bhavesh Kumar</dd><br><dt class="title">Published: </dt><dd class="value"> 4th Dec 2024</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">These days movies are one of the most important part of entertainment industry and back in the days you could see everyday people standing outside theatres, or watching movies in OTT platforms. But due to busy schedules not many people are watching every movie. They go over the internet and search for top rated movies and go to theatres. And creating a successful movie is no easy job. Thus, this study helps movie producers to consider what are the important factors that influence a movie to be successful. this study applied neural network model to the IMDb dataset and then due to its complex nature in order to achieve the local explainability and global explainability for the enhanced analysis, study have used SHAP (Shapley additive explanations) to analysis. </dd><hr></dl></li><li class="result-item article-light recent"><h3><a href="../doi/10.4108/eetsis.vi.3210">Dynamic Weighted and Heat-map Integrated Scalable Information Path-planning Algorithm</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis 24(4): e4</dd><br><dt class="title">Authors: </dt><dd class="value">Lei Wang, Yuan Xu, Zhihao Li, Shuhui Bi</dd><br><dt class="title">Published: </dt><dd class="value"> 4th Dec 2024</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">We, the publisher, removed Mackenzie Brown from the article: Bi, S., Li, Z., Brown, M., Xu, Y., & Wang, L. (2022). Dynamic Weighted and Heat-map Integrated Scalable Information Path-planning Algorithm. EAI Endorsed Transactions on Scalable Information Systems, 10(2), e5. https://doi.org/10.4108/eetsis.v9i5.1567 after being notified by the Research Integrity and Governance Adviser of Edith Cowan University, that the author has never been affiliated with that institution. All the authors were informed about this fact and we did not receive any explanation about it. It was not clarified whether this was an "involuntary mistake" or a "false author." Following the COPE guidelines, Mackenzie Brown was REMOVED from this article because of “Potentially fake academic affiliation”. </dd><hr></dl></li><li class="result-item article-light recent"><h3><a href="../doi/10.4108/eetsis.3749">Development Model of Agriculture + Travel Industry Integration in the Context of Big Data Explore: A case study from Huyi District, Xi'an</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis 24(5): </dd><br><dt class="title">Authors: </dt><dd class="value">Pei Chao Wang, Yuan Zheng, Xiang Ying Kou</dd><br><dt class="title">Published: </dt><dd class="value"> 4th Dec 2024</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">INTRODUCTION: The new industrial model of agriculture + tourism has been developed for quite some time, however, in the rapid development of information technology, especially the algorithm is further integrated into the agriculture and tourism industry, this fusion industry has ushered in a new round of development opportunities, but with the development of human society, the traditional model of agriculture and tourism will be gradually eliminated. OBJECTIVES: This paper is aimed at developing the regional needs of agriculture + tourism industry, using advanced big data technology and algorithmic technology to follow the pace of the times, in-depth understanding of the current social needs of agriculture + tourism, so as to better develop their own industries. METHODS:Through the algorithmic technology to analyze the agro-tourism model that is currently being developed in Xi'an, to analyze the problems that arise in the process of its development, and to use the background of big data and clustering algorithmic technology to put forward the corresponding targeted improvement strategies. RESULTS: Utilizing Shuangyi District in Xi'an City as a case study to apply the theory and explore new development paths. CONCLUSION: Shuangyi District, Xi'an City, is rich in soil and water resources, so it has a high level of agricultural development and a favorable geographic location, and also has a huge potential market in tourism. With the support of big data technology, the analysis of the current market demand and the development of local natural and human resources on the basis of maximizing the preservation of the original ecology can promote the development of the local economy. </dd><hr></dl></li><li class="result-item article-light recent"><h3><a href="../doi/10.4108/eetsis.7431">High-Order Local Clustering on Hypergraphs</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis 24(6): </dd><br><dt class="title">Authors: </dt><dd class="value">Jingtian Wei, Yu Zhang, Qi Luo, Zhengyi Yang, Wenjie Zhang, Lu Qin</dd><br><dt class="title">Published: </dt><dd class="value"> 4th Dec 2024</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">Graphs are a commonly used model in data mining to represent complex relationships, with nodes representing entities and edges representing relationships. However, graphs have limitations in modeling high-order relationships. In contrast, hypergraphs offer a more versatile representation, allowing edges to join any number of nodes. This capability empowers hypergraphs to model multiple relationships and capture high-order information present in real-world applications. We focus on the problem of local clustering in hypergraphs, which computes a cluster near a given seed node. Although extensively explored in the context of graphs, this problem has received less attention for hypergraphs. Current methods often directly extend graph-based local clustering to hypergraphs, overlooking their inherent high-order features and resulting in low-quality local clusters. To address this, we propose an effective hypergraph local clustering model. This model introduces a novel conductance measurement that leverages the high-order properties of hypergraphs to assess cluster quality. Based on this new definition of hypergraph conductance, we propose a greedy algorithm to find local clusters in real time. Experimental evaluations and case studies on real-world datasets demonstrate the effectiveness of the proposed methods. </dd><hr></dl></li><li class="result-item article-light recent"><h3><a href="../doi/10.4108/eetsis.5234">Study on Evaluation of Execution Capability Based on Artificial Intelligence CIPP Model</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis 24(4): </dd><br><dt class="title">Authors: </dt><dd class="value">Hui Dong</dd><br><dt class="title">Published: </dt><dd class="value">26th Nov 2024</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">INTRODUCTION: The rapid change in artificial intelligence has evaluated ideological and political education ability in colleges and universities as a significant challenge. OBJECTIVES: To assess the level of competence of universities in ideological and political education to determine the effectiveness and efficacy of educational programs and to provide a basis for improving and upgrading academic competence. METHODS: Based on the CIPP model, the author constructed an index system and selected a suitable evaluation model to conduct a study on the evaluation of ideological and political competence of colleges and universities in the context of Artificial Intelligence, which helps to understand the background conditions, resource allocation, teaching activities and quality of teaching of educational programs, as well as the level of ideological and political literacy of the students and their achievements. RESULTS: The evaluation results show that this kind of evaluation research helps to improve and enhance the capacity of ideological and political education in colleges and universities, and at the same time, it can dig into the implementation effect of the educational program, find problems and shortcomings, and promote the continuous improvement of the educational program. CONCLUSION: Through evaluation, the quality and level of ideological and political education in colleges and universities can improve students' ideological and political literacy and sense of social responsibility. In addition, based on this, it makes the development of ideological and political ability in colleges and universities can be better adapted to the era of artificial intelligence. </dd><hr></dl></li><li class="result-item article-light recent"><h3><a href="../doi/10.4108/eetsis.4550">Comparative analysis of performance of AutoML algorithms: Classification model of payment arrears in students of a private university</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis 24(4): </dd><br><dt class="title">Authors: </dt><dd class="value">Henry Villarreal-Torres, Manuel Palomino-Márquez, Carmen Mejía-Murillo, Oscar Cruz-Cruz, Julio Ángeles-Morales, Reyna Escobedo-Zarzosa, Manuel Urcia-Quispe, Gumercindo Flores-Reyes, Miguel Ángel Solar-Jara, Jenny Cano-Mejía</dd><br><dt class="title">Published: </dt><dd class="value">26th Nov 2024</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">The impact of artificial intelligence in our society is important due to the innovation of processes through data science to know the academic and sociodemographic factors that contribute to late payments in university students, to identify them and make timely decisions for implementing prevention and correction programs, avoiding student dropout due to this economic problem, and ensuring success in their education in a meaningful and focused way. In this sense, the research aims to compare the performance metrics of classification models for late payments in students of a private university by using AutoML algorithms from various existing platforms and solutions such as AutoKeras, AutoGluon, HyperOPT, MLJar, and H2O in a data set consisting of 8,495 records and the application of data balancing techniques. From the implementation and execution of various algorithms, similar metrics have been obtained based on the parameters and optimization functions used automatically by each tool, providing better performance to the H2O platform through the Stacked Ensemble algorithm with metrics accuracy = 0.778. F1 = 0.870, recall = 0.904 and precision = 0.839. The research can be extended to other contexts or areas of knowledge due to the growing interest in automated machine learning, providing researchers with a valuable tool in data science without the need for deep knowledge. </dd><hr></dl></li><li class="result-item article-light recent"><h3><a href="../doi/10.4108/eetsis.4386">Topic Modelling Analysis to Explore Policy Considerations Regarding the Practical Introduction of Affirmative Action in the Field of Education</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis 24(4): </dd><br><dt class="title">Authors: </dt><dd class="value">Ji-Hyun Jang</dd><br><dt class="title">Published: </dt><dd class="value">25th Nov 2024</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">The aim of this study is to explore the policy considerations that should be taken into account regarding the practical introduction of affirmative action policies in the field of education. For this purpose, we analysed the 100 most relevant YouTube videos produced between 2015 and 2023 using network analysis, the aim being to utilize the material they provide on affirmative action so as to reflect this in future education policies. As a result, nine key policy considerations that should be considered when introducing affirmative action policies in the field of education were derived. </dd><hr></dl></li><li class="result-item article-light recent"><h3><a href="../doi/10.4108/eetsis.4217">Design and Application of Evaluation Method for Civics Classroom Based on CRITIC Fuzzy Algorithm</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis 24(1): </dd><br><dt class="title">Authors: </dt><dd class="value">Zhanyu Chang</dd><br><dt class="title">Published: </dt><dd class="value">24th Oct 2024</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">INTRODUCTION: Through in-depth study of Civics classroom evaluation, it can provide teachers with scientific evaluation indexes and methods, improve teaching quality and effect, promote the overall development of students, and also promote the professional growth of teachers. OBJECTIVES: Based on critical and fuzzy comprehensive evaluation methods, this study aims to study the effectiveness and accuracy of the evaluation methods in Civics and Politics classrooms. Based on the CRITIC method, the fuzzy comprehensive evaluation method was introduced to solve the problem of subjectivity and uncertainty in the evaluation process. METHODS: Various research methods were used, including observation, interviews, and questionnaires. By comprehensively analyzing the students' performance, feedback, and assessment, and the teachers' pre-course preparation, the advantages and improvement directions of Civics classroom teaching can be accurately evaluated using the CRITIC and fuzzy comprehensive evaluation methods. RESULTS: The study results show that the CRITIC and fuzzy comprehensive evaluation methods can provide a more comprehensive, accurate, and objective evaluation of the Civics classroom. The undefined complete evaluation method plays an essential role in dealing with ambiguity and uncertainty in the evaluation process, making the evaluation results more objective and reliable. CONCLUSION: The Civics classroom evaluation method based on the CRITIC and fuzzy comprehensive evaluation methods is adequate and accurate. These findings strongly support improving the quality of Civics classroom teaching and enhancing students' learning outcomes. </dd><hr></dl></li><li class="result-item article-light recent"><h3><a href="../doi/10.4108/eetsis.6086">A New Hybrid COA-OOA Based Task Scheduling and Fuzzy Logic Approach to Increase Fault Tolerance in Cloud Computing</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis 24(6): </dd><br><dt class="title">Authors: </dt><dd class="value">Abhishek Swaroop, Vineet Goel, Manoj Kumar Malik</dd><br><dt class="title">Published: </dt><dd class="value">27th Jun 2024</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">INTRODUCTION: Technology is made available to customers worldwide through a distributed computing architecture called cloud computing. In the cloud paradigm, there is a risk of single-point failures, in order to prevent errors and gain confidence from consumers in their cloud services, one problem facing cloud providers is efficiently scheduling tasks. OBJECTIVES: High availability and fault tolerance must be offered to clients by these services. Fuzzy logic and hybrid COA-OOA are used in this study proposed fault-tolerant work scheduling algorithm. Jobs given by users and virtual machines are considered as input for this proposed approach. METHODS: The given tasks are initially scheduled utilizing the FIFO order. Then, it is rescheduled utilizing the Hybrid Coati Optimization Algorithm (COA) - Osprey Optimization Algorithm (OOA) for scheduling the task based on priority. RESULTS: This scheduled job is assigned to the VM for further execution. If the jobs are not executed successfully, then fault tolerant mechanism is carried out. Faults are recognized by employing fuzzy logic in this proposed approach. CONCLUSION: This proposed approach attains 62 sec response time, 61 sec of makespan and 98% success rate. Thus, this proposed approach is the best choice for efficient task scheduling with fault tolerant mechanism. </dd><hr></dl></li><li class="result-item article-light recent"><h3><a href="../doi/10.4108/eetsis.5667">A hybrid intrusion detection system with K-means and CNN+LSTM</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis 24(6): </dd><br><dt class="title">Authors: </dt><dd class="value">Haifeng Lv, Yong Ding</dd><br><dt class="title">Published: </dt><dd class="value">27th Jun 2024</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">Intrusion detection system (IDS) plays an important role as it provides an efficient mechanism to prevent or mitigate cyberattacks. With the recent advancement of artificial intelligence (AI), there have been many deep learning methods for intrusion anomaly detection to improve network security. In this research, we present a novel hybrid framework called KCLSTM, combining the K-means clustering algorithm with convolutional neural network (CNN) and long short-term memory (LSTM) architecture for the binary classification of intrusion detection systems. Extensive experiments are conducted to evaluate the performance of the proposed model on the well-known NSL-KDD dataset in terms of accuracy, precision, recall, F1-score, detection rate (DR), and false alarm rate (FAR). The results are compared with traditional machine learning approaches and deep learning methods. The proposed model demonstrates superior performance in terms of accuracy, DR, and F1-score, showcasing its effectiveness in identifying network intrusions accurately while minimizing false positives. </dd><hr></dl></li><li class="result-item article-light popular"><h3><a href="../doi/10.4108/eetsis.4217">Design and Application of Evaluation Method for Civics Classroom Based on CRITIC Fuzzy Algorithm</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis 24(1): </dd><br><dt class="title">Authors: </dt><dd class="value">Zhanyu Chang</dd><br><dt class="title">Downloads: </dt><dd class="value">1489</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">INTRODUCTION: Through in-depth study of Civics classroom evaluation, it can provide teachers with scientific evaluation indexes and methods, improve teaching quality and effect, promote the overall development of students, and also promote the professional growth of teachers. OBJECTIVES: Based on critical and fuzzy comprehensive evaluation methods, this study aims to study the effectiveness and accuracy of the evaluation methods in Civics and Politics classrooms. Based on the CRITIC method, the fuzzy comprehensive evaluation method was introduced to solve the problem of subjectivity and uncertainty in the evaluation process. METHODS: Various research methods were used, including observation, interviews, and questionnaires. By comprehensively analyzing the students' performance, feedback, and assessment, and the teachers' pre-course preparation, the advantages and improvement directions of Civics classroom teaching can be accurately evaluated using the CRITIC and fuzzy comprehensive evaluation methods. RESULTS: The study results show that the CRITIC and fuzzy comprehensive evaluation methods can provide a more comprehensive, accurate, and objective evaluation of the Civics classroom. The undefined complete evaluation method plays an essential role in dealing with ambiguity and uncertainty in the evaluation process, making the evaluation results more objective and reliable. CONCLUSION: The Civics classroom evaluation method based on the CRITIC and fuzzy comprehensive evaluation methods is adequate and accurate. These findings strongly support improving the quality of Civics classroom teaching and enhancing students' learning outcomes. </dd><hr></dl></li><li class="result-item article-light popular"><h3><a href="../doi/10.4108/eetsis.v10i3.2950">Towards Happy Housework: Scenario-Based Experience Design for a Household Cleaning Robotic System</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis 23(3): e12</dd><br><dt class="title">Authors: </dt><dd class="value">Yichen Lu, Zheng Liao</dd><br><dt class="title">Downloads: </dt><dd class="value">1411</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">INTRODUCTION: In the interwoven trend of the experience economy and advanced information technology, user experience becomes the substantial value of an interactive system. As one of the early innovations of a smart home, the current design of household cleaning robots is still driven by technology with a focus on pragmatic quality rather than the experiential value of a robotic system. OBJECTIVES: This paper aims to uplift the design vision of a cleaning robot from an automatic household appliance towards a meaningful robotic system engaging users in happy housework. METHODS: Theoretically, experience design and scenario-based design methods were combined into a specific design framework for domestic cleaning robotic systems. Based on the user study and technology trend analysis, we first set three experience goals (immersion, trust, and inspiration) to drive the design process, then chose 3D point cloud and AI recognition as backup technologies and afterwards extracted three main design scenarios (scanning and mapping, intelligent cleaning, and live control). RESULTS: The design features multi-view switching, a combination of animation rendering and real scene, fixed-point cleaning, map management, lens control and flexible remote, and shooting modes are proposed. Seventy-one participants evaluated the concept with online AttrakDiff questionnaires. The results indicate the targeted experience is fulfilled in the design concept. CONCLUSION: By integrating experience design and scenario-based design methods with technology trend analysis, designers can envision experiential scenarios of meaningful life and potentially expand the design opportunity space of interactive systems. </dd><hr></dl></li><li class="result-item article-light popular"><h3><a href="../doi/10.4108/eetsis.v10i3.2823">Data Transmission of Digital Grid Assisted by Intelligent Relaying</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis 23(3): e11</dd><br><dt class="title">Authors: </dt><dd class="value">Binyu Xie, Yuda Li, Chun Yang, Shuangbai He, Jiaqi Zhao</dd><br><dt class="title">Downloads: </dt><dd class="value">1230</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">In this paper, we study the relaying and cache aided digital grid data transmission, where the relaying may be equipped by caching or not, depending on specific applications. For both cases, we evaluate the impact of relaying and caching on the system performance of digital grid data transmission through theoretical derivation. To this end, an analytical expression on the outage probability is firstly derived for the data transmission. We then provide an asymptotic expression on the system outage probability. Finally, some simulation results are provided to verify the correctness of the derived analysis on the system performance, and show the impact of relaying and caching on the data transmission of digital grid system. In particular, the usage of caching at the relaying can help strengthen the data transmission performance of the considered system effectively. The results in this paper could provide some reference to the development of wireless transmission and scalable information systems. </dd><hr></dl></li><li class="result-item article-light popular"><h3><a href="../doi/10.4108/eetsis.v10i3.2680">A Technique for Cluster Head Selection in Wireless Sensor Networks Using African Vultures Optimization Algorithm</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis 23(3): e9</dd><br><dt class="title">Authors: </dt><dd class="value">Gurbinder Singh Brar, Vipan Kusla</dd><br><dt class="title">Downloads: </dt><dd class="value">1188</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">INTRODUCTION: Wireless Sensor Network (WSN) has caught the interest of researchers due to the rising popularity of Internet of things(IOT) based smart products and services. In challenging environmental conditions, WSN employs a large number of nodes with limited battery power to sense and transmit data to the base station(BS). Direct data transmission to the BS uses a lot of energy in these circumstances. Selecting the CH in a clustered WSN is considered to be an NP-hard problem. OBJECTIVES: The objective of this work to provide an effective cluster head selection method that minimize the overall network energy consumption, improved throughput with the main goal of enhanced network lifetime. METHODS: In this work, a meta heuristic based cluster head selection technique is proposed that has shown an edge over the other state of the art techniques. Cluster compactness, intra-cluster distance, and residual energy are taken into account while choosing CH using multi-objective function. Once the CHs have been identified, data transfer from the CHs to the base station begins. The residual energy of the nodes is finally updated during the data transmission begins. RESULTS: An analysis of the results has been performed based on average energy consumption, total energy consumption, network lifetime and throughput using two different WSN scenarios. Also, a comparison of the performance has been made other techniques namely Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Atom Search Optimization (ASO), Gorilla Troop Optimization (GTO), Harmony Search (HS), Wild Horse Optimization (WHO), Particle Swarm Optimization (PSO), Firefly Algorithm (FA) and Biogeography Based Optimization (BBO). The findings show that AVOA's first node dies at round 1391 in Scenario-1 and round 1342 in Scenario-2 which is due to lower energy consumption by the sensor nodes thus increasing lifespan of the WSN network. CONCLUSION: As per the findings, the proposed technique outperforms ABC, ACO, ASO, GTO, HS, WHO, PSO, FA, and BBO in terms of performance evaluation parameters and boosting the reliability of networks over the other state of art techniques. </dd><hr></dl></li><li class="result-item article-light popular"><h3><a href="../doi/10.4108/eetsis.v10i3.2677">Google Maps Data Analysis of Clothing Brands in South Punjab, Pakistan</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis 23(3): e10</dd><br><dt class="title">Authors: </dt><dd class="value">Majdah Alvi, Muhammad Ahmad, Kazim Jawad, Muhammad Bux Alvi</dd><br><dt class="title">Downloads: </dt><dd class="value">1186</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">The Internet is a popular and first-hand source of data about products and services. Before buying a product, people try to gain quick insight by scanning through online reviews about a targeted product. However, searching for a product, collecting all the relevant information, and reaching a decision is a tedious task that needs to be automated. Such composed decision-assisting text data analysis systems are not conveniently available worldwide. Such systems are a dream for major cities of South Punjab, such as Bahawalpur, Multan, and Rahimyar khan. This scenario creates a gap that needs to be filled. In this work, the popularity of clothing brands in three cities of south Punjab has been assessed by analysing the brand's popularity using sentiment analysis by prioritizing brands based on organic feedback from their potential customers. This study uses a combination of quantitative and qualitative research to examine online reviews from Google Maps. The task is accomplished by applying machine learning techniques, Logistic Regression (LR), and Support Vector Machine (SVM), on Google Maps reviews data using the n-gram feature extraction approach. The SVM algorithm proved to be better than others with the uni-bi-trigram features extraction method, achieving an average of 80.93% accuracy. </dd><hr></dl></li><li class="result-item article-light popular"><h3><a href="../doi/10.4108/eetsis.v10i3.2697">A Novel Approach for Prediction of Gestational Diabetes based on Clinical Signs and Risk Factors</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis 23(3): e8</dd><br><dt class="title">Authors: </dt><dd class="value">N. Meghana Preethi, Shiva Shankar Reddy , Mahesh Gadiraju, V.V.R.Maheswara Rao</dd><br><dt class="title">Downloads: </dt><dd class="value">1180</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">Gestational diabetes mellitus occurs due to high glucose levels in the blood. Pregnant women are affected by this type of diabetes. A blood test is to be performed to identify diabetes. The Oral Glucose Tolerance Test (OGTT) is a blood test performed between the 24th and 28th week of pregnancy that is necessary to identify and overcome the side effects of GDM. The main objective of this work is to train a model by utilizing the training data, evaluate the trained model using the test data, and compare existing machine learning algorithms with a Gradient boosting machine (GBM) to achieve a better model for the effective prediction of gestational diabetes. In this work, the analysis was done with a few existing algorithms and the Extreme learning machine and Gradient boosting techniques. The k-fold cross-validation technique is applied with values of k as 3, 5, and 10 to obtain better performance. The existing algorithms implemented are the Naive Bayes classifier, Support Vector Machine, K-Nearest Neighbour, ID3, CART and J48. The proposed algorithms are Gradient boosting and ELM. These algorithms are implemented in R programming. The metrics like accuracy, kappa statistic, sensitivity/Recall, specificity, precision, f-measure and AUC are used to compare all the algorithms. GBM has obtained better performance than existing algorithms. Then finally, GBM is compared with the other proposed robust Machine Learning algorithm, namely the Extreme learning machine, and the GBM performed better. So, It is recommended to use a gradient-boosting algorithm to predict gestational diabetes effectively. </dd><hr></dl></li><li class="result-item article-light popular"><h3><a href="../doi/10.4108/eetsis.4067">ALGORITHMIC LITERACY: Generative Artificial Intelligence Technologies for Data Librarians</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis 24(2): </dd><br><dt class="title">Authors: </dt><dd class="value">Tibor Koltay, Helen Beatriz Frota Rozados, Adilson Pinto, Thiago Dias, Alexandre Semeler, José González, Arthur Oliveira</dd><br><dt class="title">Downloads: </dt><dd class="value">1140</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">INTRODUCTION: Artificial intelligence (AI) is a novel type of library technology. AI technologies and the needs of data librarians are hybrid and symbiotic, because academic libraries must insert AI technologies into their information and data services. Library services need AI to interpret the context of big data. OBJECTIVES: In this context, we explore the use of the the OpenAI Codex, a deep learning model trained on Python code from repositories, to generate code scripts for data librarians. This investigation examines the practices, models, and methodologies for obtaining code script insights from complex code environments linked to AI GPT technologies. METHODS: The proposed AI-powered method aims to assist data librarians in creating code scripts using Python libraries and plugins such as the integrated development environment PyCharm, with additional support from the Machinet AI and Bito AI plugins. The process involves collaboration between the data librarian and the AI agent, with the librarian providing a natural language description of the programming problem and the OpenAI Codex generating the solution code in Python. RESULTS: Five specific web-scraping problems are presented. The scripts demonstrate how to extract data, calculate metrics, and write the results to files. CONCLUSION: Overall, this study highlights the application of AI in assisting data librarians with code script creation for web scraping tasks. AI may be a valuable resource for data librarians dealing with big data challenges on the Web. The possibility of creating Python code with AI is of great value, as AI technologies can help data librarians work with various types of data sources. The Python code in Data Science web scraping projects uses a machine-learning model that can generate human-like code to help create and improve the library service for extracting data from a web collection. The ability of nonprogramming data librarians to use AI technologies facilitates their interactions with all types and data sources. The Python programming language has artificial intelligence modules, packages, and plugins such as the OpenAI Codex, which serialises automation and navigation in web browsers to simulate human behaviour on pages by entering passwords, selecting captcha options, collecting data, and creating different collections of datasets to be viewed. </dd><hr></dl></li><li class="result-item article-light popular"><h3><a href="../doi/10.4108/eetsis.v10i2.2948">A Chatbot Intent Classifier for Supporting High School Students</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis 22(3): e1</dd><br><dt class="title">Authors: </dt><dd class="value">Manar Alkhatib, Khaled Shaalan, Suha Khalil Assayed</dd><br><dt class="title">Downloads: </dt><dd class="value">1082</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">INTRODUCTION: An intent classification is a challenged task in Natural Language Processing (NLP) as we are asking the machine to understand our language by categorizing the users’ requests. As a result, the intent classification plays an essential role in having a chatbot conversation that understand students’ requests. OBJECTIVES: In this study, we developed a novel chatbot called “HSchatbot” for predicting the intent classifications from high school students’ enquiries. Evidently, students in high schools are the most concerned among all students about their future; thus, in this stage they need an instant support in order to prepare them to take the right decision for their career choice. METHODS: The authors in this study used the Multinomial Naive-Bayes and Random Forest classifiers for predicting the students’ enquiries, which in turn improved the performance of the classifiers by using the feature’s extractions. RESULTS: The results show that the random forest classifier performed better than Multinomial Naive-Bayes since the performance of this model is checked by using different metrics like accuracy, precision, recall and F1 score. Moreover, all showed high accuracy scores exceeding 90% in all metrics. However, the accuracy of Multinomial Naive-Bayes classifier performed much better when using CountVectorizers compared to using the TF-IDF. CONCLUSION: In the future work, the results will be analysed and investigated in order to figure out the main factors that affect the performance of Multinomial Naive-Bayes classifier, as well as evaluating the model with using a large corpus of students’ questions and enquiries. </dd><hr></dl></li><li class="result-item article-light popular"><h3><a href="../doi/10.4108/eetsis.v10i3.2837">Secure Data Processing Technology of Distribution Network OPGW Line with Edge Computing</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis 23(3): e7</dd><br><dt class="title">Authors: </dt><dd class="value">Zhongmiao Kang, Ying Zeng, Zhan Shi</dd><br><dt class="title">Downloads: </dt><dd class="value">1032</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">Promoted by information technology and scalable information systems, the network design and communication method of optical fiber composite overhead ground wire (OPGW) have been in great progress recently. As the overhead transmission line has strict requirements on the outer diameter and weight of OPGW, it is of vital importance to perform the physical-layer secure data processing for the distribution network OPGW line with edge computing. To this end, we examine a physical-layer secure distribution network OPGW with edge computing in this article, where there exists one transmitter S, one receiver D, one authorized legitimate monitor LM, and an interfering node I. To better analyze the system performance, we firstly give the definition of the system outage probability, based on the secure data rate. Then, we evaluate the system performance for the distribution network OPGW, by deriving analytical outage probability of secure data processing, to facilitate the system performance evaluation of secure data processing in the entire SNR regime. Finally, we demonstrate some simulation results to validate the analytical results on the physical-layer secure distribution network OPGW line with edge computing. </dd><hr></dl></li><li class="result-item article-light popular"><h3><a href="../doi/10.4108/eetsis.v10i3.2878"> Matrix Completion via Successive Low-rank Matrix Approximation</a></h3><dl class="metadata"><dt class="title">Appears in: </dt><dd class="value">sis 23(3): e6</dd><br><dt class="title">Authors: </dt><dd class="value">Zeyao Mo, Jin Wang</dd><br><dt class="title">Downloads: </dt><dd class="value">1025</dd><br><dt class="title">Abstract: </dt><dd class="value abstract">In this paper, a successive low-rank matrix approximation algorithm is presented for the matrix completion (MC) based on hard thresholding method, which approximate the optimal low-rank matrix from rank-one matrix step by step. The algorithm enables the distance between the matrix with the observed elements and the projection on low-rank manifold to be minimum. The optimal low-rank matrix with observed elements is obtained when the distance is zero. In theory, convergence and convergent error of the new algorithm are analyzed in detail. Furthermore, some numerical experiments show that the algorithm is more effective in CPU time and precision than the orthogonal rank-one matrix pursuit(OR1MP) algorithm and the augmented Lagrange multiplier (ALM) method when the sampling rate is low. </dd><hr></dl></li></ul></div></div></section><section class="publication-info"><dl class="metadata"><dt class="title">Publisher</dt> <dd class="value">EAI</dd> <dt class="title">ISSN</dt> <dd class="value">2032-9407</dd> <dt class="title">Number of Volumes</dt> <dd class="value">11</dd></dl><dl class="metadata"><dt class="title">Last Published</dt> <dd class="value">2024-10-05</dd></dl></section></div></section></form></section></section><div class="clear"></div><footer><div class="links"><a href="https://www.ebsco.com/" target="_blank"><img class="logo ebsco-logo" src="/images/ebsco.png" alt="EBSCO"></a><a href="https://www.proquest.com/" target="_blank"><img class="logo proquest-logo" src="/images/proquest.png" alt="ProQuest"></a><a href="https://dblp.uni-trier.de/db/journals/publ/icst.html" target="_blank"><img class="logo dblp-logo" src="/images/dblp.png" alt="DBLP"></a><a href="https://doaj.org/search?source=%7B%22query%22%3A%7B%22filtered%22%3A%7B%22filter%22%3A%7B%22bool%22%3A%7B%22must%22%3A%5B%7B%22term%22%3A%7B%22index.publisher.exact%22%3A%22European%20Alliance%20for%20Innovation%20(EAI)%22%7D%7D%5D%7D%7D%2C%22query%22%3A%7B%22query_string%22%3A%7B%22query%22%3A%22european%20alliance%20for%20innovation%22%2C%22default_operator%22%3A%22AND%22%2C%22default_field%22%3A%22index.publisher%22%7D%7D%7D%7D%7Dj" target="_blank"><img class="logo doaj-logo" src="/images/doaj.jpg" alt="DOAJ"></a><a href="https://www.portico.org/publishers/eai/" target="_blank"><img class="logo portico-logo" src="/images/portico.png" alt="Portico"></a><a href="http://eai.eu/" target="_blank"><img class="logo eai-logo" src="/images/eai.png"></a></div></footer></div><div class="footer-container"><div class="footer-width"><div class="footer-column logo-column"><a href="https://eai.eu/"><img src="https://eudl.eu/images/logo_new-1-1.png" alt="EAI Logo"></a></div><div class="footer-column"><h4>About EAI</h4><ul><li><a href="https://eai.eu/who-we-are/">Who We Are</a></li><li><a href="https://eai.eu/leadership/">Leadership</a></li><li><a href="https://eai.eu/research-areas/">Research Areas</a></li><li><a href="https://eai.eu/partners/">Partners</a></li><li><a href="https://eai.eu/media-center/">Media Center</a></li></ul></div><div class="footer-column"><h4>Community</h4><ul><li><a href="https://eai.eu/eai-community/">Membership</a></li><li><a href="https://eai.eu/conferences/">Conference</a></li><li><a href="https://eai.eu/recognition/">Recognition</a></li><li><a href="https://eai.eu/corporate-sponsorship">Sponsor Us</a></li></ul></div><div class="footer-column"><h4>Publish with EAI</h4><ul><li><a href="https://eai.eu/publishing">Publishing</a></li><li><a href="https://eai.eu/journals/">Journals</a></li><li><a href="https://eai.eu/proceedings/">Proceedings</a></li><li><a href="https://eai.eu/books/">Books</a></li><li><a href="https://eudl.eu/">EUDL</a></li></ul></div></div></div><script type="text/javascript" src="https://eudl.eu/js/gacode.js"></script><script src="/js/highlight.pack.js"></script><script>hljs.initHighlightingOnLoad();</script><script type="application/ld+json">{"@context":"http://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"item":{"@id":"http://eudl.eu","name":"Home","image":null}},{"@type":"ListItem","position":2,"item":{"@id":"http://eudl.eu/journals","name":"Journals","image":null}},{"@type":"ListItem","position":3,"item":{"@id":"http://eudl.eu/journal/sis","name":"sis","image":null}}]}</script></body></html>