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measures</title> <meta name="description" content="Search results for: similarity measures"> <meta name="keywords" content="similarity measures"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="similarity measures"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 4259</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: similarity measures</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4259</span> Agglomerative Hierarchical Clustering Using the T胃 Family of Similarity Measures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salima%20Kouici">Salima Kouici</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelkader%20Khelladi"> Abdelkader Khelladi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we begin with the presentation of the T胃 family of usual similarity measures concerning multidimensional binary data. Subsequently, some properties of these measures are proposed. Finally, the impact of the use of different inter-elements measures on the results of the Agglomerative Hierarchical Clustering Methods is studied. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binary%20data" title="binary data">binary data</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20measure" title=" similarity measure"> similarity measure</a>, <a href="https://publications.waset.org/abstracts/search?q=T%CE%B8%20measures" title=" T胃 measures"> T胃 measures</a>, <a href="https://publications.waset.org/abstracts/search?q=agglomerative%20hierarchical%20clustering" title=" agglomerative hierarchical clustering"> agglomerative hierarchical clustering</a> </p> <a href="https://publications.waset.org/abstracts/13108/agglomerative-hierarchical-clustering-using-the-tth-family-of-similarity-measures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13108.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">481</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4258</span> Empirical Study of Partitions Similarity Measures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdelkrim%20Alfalah">Abdelkrim Alfalah</a>, <a href="https://publications.waset.org/abstracts/search?q=Lahcen%20Ouarbya"> Lahcen Ouarbya</a>, <a href="https://publications.waset.org/abstracts/search?q=John%20Howroyd"> John Howroyd</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper investigates and compares the performance of four existing distances and similarity measures between partitions. The partition measures considered are Rand Index (RI), Adjusted Rand Index (ARI), Variation of Information (VI), and Normalised Variation of Information (NVI). This work investigates the ability of these partition measures to capture three predefined intuitions: the variation within randomly generated partitions, the sensitivity to small perturbations, and finally the independence from the dataset scale. It has been shown that the Adjusted Rand Index performed well overall, with regards to these three intuitions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=comparing%20partitions" title=" comparing partitions"> comparing partitions</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20measure" title=" similarity measure"> similarity measure</a>, <a href="https://publications.waset.org/abstracts/search?q=partition%20distance" title=" partition distance"> partition distance</a>, <a href="https://publications.waset.org/abstracts/search?q=partition%20metric" title=" partition metric"> partition metric</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20between%20partitions" title=" similarity between partitions"> similarity between partitions</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20comparison." title=" clustering comparison."> clustering comparison.</a> </p> <a href="https://publications.waset.org/abstracts/143607/empirical-study-of-partitions-similarity-measures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143607.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">202</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4257</span> Clustering of Association Rules of ISIS & Al-Qaeda Based on Similarity Measures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tamanna%20Goyal">Tamanna Goyal</a>, <a href="https://publications.waset.org/abstracts/search?q=Divya%20Bansal"> Divya Bansal</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanjeev%20Sofat"> Sanjeev Sofat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In world-threatening terrorist attacks, where early detection, distinction, and prediction are effective diagnosis techniques and for functionally accurate and precise analysis of terrorism data, there are so many data mining & statistical approaches to assure accuracy. The computational extraction of derived patterns is a non-trivial task which comprises specific domain discovery by means of sophisticated algorithm design and analysis. This paper proposes an approach for similarity extraction by obtaining the useful attributes from the available datasets of terrorist attacks and then applying feature selection technique based on the statistical impurity measures followed by clustering techniques on the basis of similarity measures. On the basis of degree of participation of attributes in the rules, the associative dependencies between the attacks are analyzed. Consequently, to compute the similarity among the discovered rules, we applied a weighted similarity measure. Finally, the rules are grouped by applying using hierarchical clustering. We have applied it to an open source dataset to determine the usability and efficiency of our technique, and a literature search is also accomplished to support the efficiency and accuracy of our results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=association%20rules" title="association rules">association rules</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20measure" title=" similarity measure"> similarity measure</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20approaches" title=" statistical approaches "> statistical approaches </a> </p> <a href="https://publications.waset.org/abstracts/53364/clustering-of-association-rules-of-isis-al-qaeda-based-on-similarity-measures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53364.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">320</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4256</span> Tool for Determining the Similarity between Two Web Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Doru%20Anastasiu%20Popescu">Doru Anastasiu Popescu</a>, <a href="https://publications.waset.org/abstracts/search?q=Raducanu%20Dragos%20Ionut"> Raducanu Dragos Ionut</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper the presentation of a tool which measures the similarity between two websites is made. The websites are compound only from webpages created with HTML. The tool uses three ways of calculating the similarity between two websites based on certain results already published. The first way compares all the webpages within a website, the second way compares a webpage with all the pages within the second website and the third way compares two webpages. Java programming language and technologies such as spring, Jsoup, log4j were used for the implementation of the tool. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Java" title="Java">Java</a>, <a href="https://publications.waset.org/abstracts/search?q=Jsoup" title=" Jsoup"> Jsoup</a>, <a href="https://publications.waset.org/abstracts/search?q=HTM" title=" HTM"> HTM</a>, <a href="https://publications.waset.org/abstracts/search?q=spring" title=" spring"> spring</a> </p> <a href="https://publications.waset.org/abstracts/48293/tool-for-determining-the-similarity-between-two-web-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48293.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">385</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4255</span> A Similarity Measure for Classification and Clustering in Image Based Medical and Text Based Banking Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20P.%20Sandesh">K. P. Sandesh</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20H.%20Suman"> M. H. Suman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text processing plays an important role in information retrieval, data-mining, and web search. Measuring the similarity between the documents is an important operation in the text processing field. In this project, a new similarity measure is proposed. To compute the similarity between two documents with respect to a feature the proposed measure takes the following three cases into account: (1) The feature appears in both documents; (2) The feature appears in only one document and; (3) The feature appears in none of the documents. The proposed measure is extended to gauge the similarity between two sets of documents. The effectiveness of our measure is evaluated on several real-world data sets for text classification and clustering problems, especially in banking and health sectors. The results show that the performance obtained by the proposed measure is better than that achieved by the other measures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=document%20classification" title="document classification">document classification</a>, <a href="https://publications.waset.org/abstracts/search?q=document%20clustering" title=" document clustering"> document clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=classifiers" title=" classifiers"> classifiers</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20algorithms" title=" clustering algorithms"> clustering algorithms</a> </p> <a href="https://publications.waset.org/abstracts/22708/a-similarity-measure-for-classification-and-clustering-in-image-based-medical-and-text-based-banking-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22708.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">518</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4254</span> Static vs. Stream Mining Trajectories Similarity Measures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Musaab%20Riyadh">Musaab Riyadh</a>, <a href="https://publications.waset.org/abstracts/search?q=Norwati%20Mustapha"> Norwati Mustapha</a>, <a href="https://publications.waset.org/abstracts/search?q=Dina%20Riyadh"> Dina Riyadh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Trajectory similarity can be defined as the cost of transforming one trajectory into another based on certain similarity method. It is the core of numerous mining tasks such as clustering, classification, and indexing. Various approaches have been suggested to measure similarity based on the geometric and dynamic properties of trajectory, the overlapping between trajectory segments, and the confined area between entire trajectories. In this article, an evaluation of these approaches has been done based on computational cost, usage memory, accuracy, and the amount of data which is needed in advance to determine its suitability to stream mining applications. The evaluation results show that the stream mining applications support similarity methods which have low computational cost and memory, single scan on data, and free of mathematical complexity due to the high-speed generation of data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=global%20distance%20measure" title="global distance measure">global distance measure</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20distance%20measure" title=" local distance measure"> local distance measure</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20trajectory" title=" semantic trajectory"> semantic trajectory</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20dimension" title=" spatial dimension"> spatial dimension</a>, <a href="https://publications.waset.org/abstracts/search?q=stream%20data%20mining" title=" stream data mining"> stream data mining</a> </p> <a href="https://publications.waset.org/abstracts/94763/static-vs-stream-mining-trajectories-similarity-measures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94763.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">396</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4253</span> Multi-Objective Optimal Threshold Selection for Similarity Functions in Siamese Networks for Semantic Textual Similarity Tasks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kriuk%20Boris">Kriuk Boris</a>, <a href="https://publications.waset.org/abstracts/search?q=Kriuk%20Fedor"> Kriuk Fedor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comparative study of fundamental similarity functions for Siamese networks in semantic textual similarity (STS) tasks. We evaluate various similarity functions using the STS Benchmark dataset, analyzing their performance and stability. Additionally, we introduce a multi-objective approach for optimal threshold selection. Our findings provide insights into the effectiveness of different similarity functions and offer a straightforward method for threshold selection optimization, contributing to the advancement of Siamese network architectures in STS applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=siamese%20networks" title="siamese networks">siamese networks</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20textual%20similarity" title=" semantic textual similarity"> semantic textual similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20functions" title=" similarity functions"> similarity functions</a>, <a href="https://publications.waset.org/abstracts/search?q=STS%20benchmark%20dataset" title=" STS benchmark dataset"> STS benchmark dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=threshold%20selection" title=" threshold selection"> threshold selection</a> </p> <a href="https://publications.waset.org/abstracts/187407/multi-objective-optimal-threshold-selection-for-similarity-functions-in-siamese-networks-for-semantic-textual-similarity-tasks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/187407.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">38</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4252</span> Impact of Similarity Ratings on Human Judgement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ian%20A.%20McCulloh">Ian A. McCulloh</a>, <a href="https://publications.waset.org/abstracts/search?q=Madelaine%20Zinser"> Madelaine Zinser</a>, <a href="https://publications.waset.org/abstracts/search?q=Jesse%20Patsolic"> Jesse Patsolic</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Ramos"> Michael Ramos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recommender systems are a common artificial intelligence (AI) application. For any given input, a search system will return a rank-ordered list of similar items. As users review returned items, they must decide when to halt the search and either revise search terms or conclude their requirement is novel with no similar items in the database. We present a statistically designed experiment that investigates the impact of similarity ratings on human judgement to conclude a search item is novel and halt the search. 450 participants were recruited from Amazon Mechanical Turk to render judgement across 12 decision tasks. We find the inclusion of ratings increases the human perception that items are novel. Percent similarity increases novelty discernment when compared with star-rated similarity or the absence of a rating. Ratings reduce the time to decide and improve decision confidence. This suggests the inclusion of similarity ratings can aid human decision-makers in knowledge search tasks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ratings" title="ratings">ratings</a>, <a href="https://publications.waset.org/abstracts/search?q=rankings" title=" rankings"> rankings</a>, <a href="https://publications.waset.org/abstracts/search?q=crowdsourcing" title=" crowdsourcing"> crowdsourcing</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20studies" title=" empirical studies"> empirical studies</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20studies" title=" user studies"> user studies</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20measures" title=" similarity measures"> similarity measures</a>, <a href="https://publications.waset.org/abstracts/search?q=human-centered%20computing" title=" human-centered computing"> human-centered computing</a>, <a href="https://publications.waset.org/abstracts/search?q=novelty%20in%20information%20retrieval" title=" novelty in information retrieval"> novelty in information retrieval</a> </p> <a href="https://publications.waset.org/abstracts/163910/impact-of-similarity-ratings-on-human-judgement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163910.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">131</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4251</span> Approximately Similarity Measurement of Web Sites Using Genetic Algorithms and Binary Trees</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Doru%20Anastasiu%20Popescu">Doru Anastasiu Popescu</a>, <a href="https://publications.waset.org/abstracts/search?q=Dan%20R%C4%83dulescu"> Dan R膬dulescu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we determine the similarity of two HTML web applications. We are going to use a genetic algorithm in order to determine the most significant web pages of each application (we are not going to use every web page of a site). Using these significant web pages, we will find the similarity value between the two applications. The algorithm is going to be efficient because we are going to use a reduced number of web pages for comparisons but it will return an approximate value of the similarity. The binary trees are used to keep the tags from the significant pages. The algorithm was implemented in Java language. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tag" title="Tag">Tag</a>, <a href="https://publications.waset.org/abstracts/search?q=HTML" title=" HTML"> HTML</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20page" title=" web page"> web page</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20value" title=" similarity value"> similarity value</a>, <a href="https://publications.waset.org/abstracts/search?q=binary%20tree" title=" binary tree"> binary tree</a> </p> <a href="https://publications.waset.org/abstracts/50460/approximately-similarity-measurement-of-web-sites-using-genetic-algorithms-and-binary-trees" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50460.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">355</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4250</span> Measuring Text-Based Semantics Relatedness Using WordNet</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Madiha%20Khan">Madiha Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sidrah%20Ramzan"> Sidrah Ramzan</a>, <a href="https://publications.waset.org/abstracts/search?q=Seemab%20Khan"> Seemab Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahzad%20Hassan"> Shahzad Hassan</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamran%20Saeed"> Kamran Saeed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Measuring semantic similarity between texts is calculating semantic relatedness between texts using various techniques. Our web application (Measuring Relatedness of Concepts-MRC) allows user to input two text corpuses and get semantic similarity percentage between both using WordNet. Our application goes through five stages for the computation of semantic relatedness. Those stages are: Preprocessing (extracts keywords from content), Feature Extraction (classification of words into Parts-of-Speech), Synonyms Extraction (retrieves synonyms against each keyword), Measuring Similarity (using keywords and synonyms, similarity is measured) and Visualization (graphical representation of similarity measure). Hence the user can measure similarity on basis of features as well. The end result is a percentage score and the word(s) which form the basis of similarity between both texts with use of different tools on same platform. In future work we look forward for a Web as a live corpus application that provides a simpler and user friendly tool to compare documents and extract useful information. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Graphviz%20representation" title="Graphviz representation">Graphviz representation</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20relatedness" title=" semantic relatedness"> semantic relatedness</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20measurement" title=" similarity measurement"> similarity measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=WordNet%20similarity" title=" WordNet similarity"> WordNet similarity</a> </p> <a href="https://publications.waset.org/abstracts/95106/measuring-text-based-semantics-relatedness-using-wordnet" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95106.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">238</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4249</span> Quick Similarity Measurement of Binary Images via Probabilistic Pixel Mapping</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adnan%20A.%20Y.%20Mustafa">Adnan A. Y. Mustafa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we present a quick technique to measure the similarity between binary images. The technique is based on a probabilistic mapping approach and is fast because only a minute percentage of the image pixels need to be compared to measure the similarity, and not the whole image. We exploit the power of the Probabilistic Matching Model for Binary Images (PMMBI) to arrive at an estimate of the similarity. We show that the estimate is a good approximation of the actual value, and the quality of the estimate can be improved further with increased image mappings. Furthermore, the technique is image size invariant; the similarity between big images can be measured as fast as that for small images. Examples of trials conducted on real images are presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20images" title="big images">big images</a>, <a href="https://publications.waset.org/abstracts/search?q=binary%20images" title=" binary images"> binary images</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20matching" title=" image matching"> image matching</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20similarity" title=" image similarity"> image similarity</a> </p> <a href="https://publications.waset.org/abstracts/89963/quick-similarity-measurement-of-binary-images-via-probabilistic-pixel-mapping" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89963.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">196</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4248</span> Resume Ranking Using Custom Word2vec and Rule-Based Natural Language Processing Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Subodh%20Chandra%20Shakya">Subodh Chandra Shakya</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajendra%20Sapkota"> Rajendra Sapkota</a>, <a href="https://publications.waset.org/abstracts/search?q=Aakash%20Tamang"> Aakash Tamang</a>, <a href="https://publications.waset.org/abstracts/search?q=Shushant%20Pudasaini"> Shushant Pudasaini</a>, <a href="https://publications.waset.org/abstracts/search?q=Sujan%20Adhikari"> Sujan Adhikari</a>, <a href="https://publications.waset.org/abstracts/search?q=Sajjan%20Adhikari"> Sajjan Adhikari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Lots of efforts have been made in order to measure the semantic similarity between the text corpora in the documents. Techniques have been evolved to measure the similarity of two documents. One such state-of-art technique in the field of Natural Language Processing (NLP) is word to vector models, which converts the words into their word-embedding and measures the similarity between the vectors. We found this to be quite useful for the task of resume ranking. So, this research paper is the implementation of the word2vec model along with other Natural Language Processing techniques in order to rank the resumes for the particular job description so as to automate the process of hiring. The research paper proposes the system and the findings that were made during the process of building the system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chunking" title="chunking">chunking</a>, <a href="https://publications.waset.org/abstracts/search?q=document%20similarity" title=" document similarity"> document similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20extraction" title=" information extraction"> information extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=word2vec" title=" word2vec"> word2vec</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20embedding" title=" word embedding"> word embedding</a> </p> <a href="https://publications.waset.org/abstracts/129534/resume-ranking-using-custom-word2vec-and-rule-based-natural-language-processing-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129534.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">158</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4247</span> A Context-Sensitive Algorithm for Media Similarity Search </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guang-Ho%20Cha">Guang-Ho Cha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a context-sensitive media similarity search algorithm. One of the central problems regarding media search is the semantic gap between the low-level features computed automatically from media data and the human interpretation of them. This is because the notion of similarity is usually based on high-level abstraction but the low-level features do not sometimes reflect the human perception. Many media search algorithms have used the Minkowski metric to measure similarity between image pairs. However those functions cannot adequately capture the aspects of the characteristics of the human visual system as well as the nonlinear relationships in contextual information given by images in a collection. Our search algorithm tackles this problem by employing a similarity measure and a ranking strategy that reflect the nonlinearity of human perception and contextual information in a dataset. Similarity search in an image database based on this contextual information shows encouraging experimental results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=context-sensitive%20search" title="context-sensitive search">context-sensitive search</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20search" title=" image search"> image search</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20ranking" title=" similarity ranking"> similarity ranking</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20search" title=" similarity search"> similarity search</a> </p> <a href="https://publications.waset.org/abstracts/65150/a-context-sensitive-algorithm-for-media-similarity-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65150.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">365</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4246</span> Recruitment Model (FSRM) for Faculty Selection Based on Fuzzy Soft</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20S.%20Thakur">G. S. Thakur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a Fuzzy Soft Recruitment Model (FSRM) for faculty selection of MHRD technical institutions. The selection criteria are based on 4-tier flexible structure in the institutions. The Advisory Committee on Faculty Recruitment (ACoFAR) suggested nine criteria for faculty in the proposed FSRM. The model Fuzzy Soft is proposed with consultation of ACoFAR based on selection criteria. The Fuzzy Soft distance similarity measures are applied for finding best faculty from the applicant pool. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20soft%20set" title="fuzzy soft set">fuzzy soft set</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20sets" title=" fuzzy sets"> fuzzy sets</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20soft%20distance" title=" fuzzy soft distance"> fuzzy soft distance</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20soft%20similarity%20measures" title=" fuzzy soft similarity measures"> fuzzy soft similarity measures</a>, <a href="https://publications.waset.org/abstracts/search?q=ACoFAR" title=" ACoFAR"> ACoFAR</a> </p> <a href="https://publications.waset.org/abstracts/12838/recruitment-model-fsrm-for-faculty-selection-based-on-fuzzy-soft" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12838.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">347</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4245</span> Review and Suggestions of the Similarity between Employee and Its Workplace</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gi%20Ryung%20Song">Gi Ryung Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Kyoung%20Seok%20Kim"> Kyoung Seok Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study reviewed the literature that focused on similarity of various characteristics such as values, personality, or demographics between employee and other elements in its organization for example employee with leader, job, and organization. We divided a body of this study into two parts and organized and demonstrated recent studies in first part. Three issues appeared in this part, which are statistical ways of measuring similarity, supervisor-subordinate similarity, and person-organization fit with person-job fit. In the latter part, based on the three issues of recent studies, we suggested three propositions about points that the recent studies missed or the studies did not orient. First proposition argued about the direction of similarity, which could also be interpreted as there is causal relation between employee and its workplace environments. Second, we suggested a consideration of eliminating common variance buried in one鈥檚 characteristics or its profiles. Third proposition was about the similarity of extra role behavior between individual and organization, and we treated this organization鈥檚 level of extra role behavior as a kind of its culture. In doing so, similarity of individual鈥檚 extra role behavior and organization鈥檚 has the meaning that individual鈥檚 congruence against their organization culture. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=similarity" title="similarity">similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=person-organization%20fit" title=" person-organization fit"> person-organization fit</a>, <a href="https://publications.waset.org/abstracts/search?q=supervisor-subordinate%20similarity" title=" supervisor-subordinate similarity"> supervisor-subordinate similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=literature%20review" title=" literature review"> literature review</a> </p> <a href="https://publications.waset.org/abstracts/54492/review-and-suggestions-of-the-similarity-between-employee-and-its-workplace" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54492.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">284</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4244</span> Cross-Dialect Sentence Transformation: A Comparative Analysis of Language Models for Adapting Sentences to British English</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shashwat%20Mookherjee">Shashwat Mookherjee</a>, <a href="https://publications.waset.org/abstracts/search?q=Shruti%20Dutta"> Shruti Dutta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study explores linguistic distinctions among American, Indian, and Irish English dialects and assesses various Language Models (LLMs) in their ability to generate British English translations from these dialects. Using cosine similarity analysis, the study measures the linguistic proximity between original British English translations and those produced by LLMs for each dialect. The findings reveal that Indian and Irish English translations maintain notably high similarity scores, suggesting strong linguistic alignment with British English. In contrast, American English exhibits slightly lower similarity, reflecting its distinct linguistic traits. Additionally, the choice of LLM significantly impacts translation quality, with Llama-2-70b consistently demonstrating superior performance. The study underscores the importance of selecting the right model for dialect translation, emphasizing the role of linguistic expertise and contextual understanding in achieving accurate translations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cross-dialect%20translation" title="cross-dialect translation">cross-dialect translation</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20models" title=" language models"> language models</a>, <a href="https://publications.waset.org/abstracts/search?q=linguistic%20similarity" title=" linguistic similarity"> linguistic similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=multilingual%20NLP" title=" multilingual NLP"> multilingual NLP</a> </p> <a href="https://publications.waset.org/abstracts/184401/cross-dialect-sentence-transformation-a-comparative-analysis-of-language-models-for-adapting-sentences-to-british-english" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184401.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">75</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4243</span> 2D Fingerprint Performance for PubChem Chemical Database</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatimah%20Zawani%20Abdullah">Fatimah Zawani Abdullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Shereena%20Mohd%20Arif"> Shereena Mohd Arif</a>, <a href="https://publications.waset.org/abstracts/search?q=Nurul%20Malim"> Nurul Malim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study of molecular similarity search in chemical database is increasingly widespread, especially in the area of drug discovery. Similarity search is an application in the field of Chemoinformatics to measure the similarity between the molecular structure which is known as the query and the structure of chemical compounds in the database. Similarity search is also one of the approaches in virtual screening which involves computational techniques and scoring the probabilities of activity. The main objective of this work is to determine the best fingerprint when compared to the other five fingerprints selected in this study using PubChem chemical dataset. This paper will discuss the similarity searching process conducted using 6 types of descriptors, which are ECFP4, ECFC4, FCFP4, FCFC4, SRECFC4 and SRFCFC4 on 15 activity classes of PubChem dataset using Tanimoto coefficient to calculate the similarity between the query structures and each of the database structure. The results suggest that ECFP4 performs the best to be used with Tanimoto coefficient in the PubChem dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=2D%20fingerprints" title="2D fingerprints">2D fingerprints</a>, <a href="https://publications.waset.org/abstracts/search?q=Tanimoto" title=" Tanimoto"> Tanimoto</a>, <a href="https://publications.waset.org/abstracts/search?q=PubChem" title=" PubChem"> PubChem</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20searching" title=" similarity searching"> similarity searching</a>, <a href="https://publications.waset.org/abstracts/search?q=chemoinformatics" title=" chemoinformatics"> chemoinformatics</a> </p> <a href="https://publications.waset.org/abstracts/15097/2d-fingerprint-performance-for-pubchem-chemical-database" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15097.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">293</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4242</span> Semantic Search Engine Based on Query Expansion with Google Ranking and Similarity Measures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Shahin">Ahmad Shahin</a>, <a href="https://publications.waset.org/abstracts/search?q=Fadi%20Chakik"> Fadi Chakik</a>, <a href="https://publications.waset.org/abstracts/search?q=Walid%20Moudani"> Walid Moudani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Our study is about elaborating a potential solution for a search engine that involves semantic technology to retrieve information and display it significantly. Semantic search engines are not used widely over the web as the majorities are still in Beta stage or under construction. Many problems face the current applications in semantic search, the major problem is to analyze and calculate the meaning of query in order to retrieve relevant information. Another problem is the ontology based index and its updates. Ranking results according to concept meaning and its relation with query is another challenge. In this paper, we are offering a light meta-engine (QESM) which uses Google search, and therefore Google鈥檚 index, with some adaptations to its returned results by adding multi-query expansion. The mission was to find a reliable ranking algorithm that involves semantics and uses concepts and meanings to rank results. At the beginning, the engine finds synonyms of each query term entered by the user based on a lexical database. Then, query expansion is applied to generate different semantically analogous sentences. These are generated randomly by combining the found synonyms and the original query terms. Our model suggests the use of semantic similarity measures between two sentences. Practically, we used this method to calculate semantic similarity between each query and the description of each page鈥檚 content generated by Google. The generated sentences are sent to Google engine one by one, and ranked again all together with the adapted ranking method (QESM). Finally, our system will place Google pages with higher similarities on the top of the results. We have conducted experimentations with 6 different queries. We have observed that most ranked results with QESM were altered with Google鈥檚 original generated pages. With our experimented queries, QESM generates frequently better accuracy than Google. In some worst cases, it behaves like Google. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semantic%20search%20engine" title="semantic search engine">semantic search engine</a>, <a href="https://publications.waset.org/abstracts/search?q=Google%20indexing" title=" Google indexing"> Google indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=query%20expansion" title=" query expansion"> query expansion</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20measures" title=" similarity measures"> similarity measures</a> </p> <a href="https://publications.waset.org/abstracts/10857/semantic-search-engine-based-on-query-expansion-with-google-ranking-and-similarity-measures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10857.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">425</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4241</span> Similarity Based Membership of Elements to Uncertain Concept in Information System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Kamel%20El-Sayed">M. Kamel El-Sayed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The process of determining the degree of membership for an element to an uncertain concept has been found in many ways, using equivalence and symmetry relations in information systems. In the case of similarity, these methods did not take into account the degree of symmetry between elements. In this paper, we use a new definition for finding the membership based on the degree of symmetry. We provide an example to clarify the suggested methods and compare it with previous methods. This method opens the door to more accurate decisions in information systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20system" title="information system">information system</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertain%20concept" title=" uncertain concept"> uncertain concept</a>, <a href="https://publications.waset.org/abstracts/search?q=membership%20function" title=" membership function"> membership function</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20relation" title=" similarity relation"> similarity relation</a>, <a href="https://publications.waset.org/abstracts/search?q=degree%20of%20similarity" title=" degree of similarity"> degree of similarity</a> </p> <a href="https://publications.waset.org/abstracts/88086/similarity-based-membership-of-elements-to-uncertain-concept-in-information-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88086.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">223</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4240</span> Top-K Shortest Distance as a Similarity Measure</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andrey%20Lebedev">Andrey Lebedev</a>, <a href="https://publications.waset.org/abstracts/search?q=Ilya%20Dmitrenok"> Ilya Dmitrenok</a>, <a href="https://publications.waset.org/abstracts/search?q=JooYoung%20Lee"> JooYoung Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Leonard%20Johard"> Leonard Johard</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Top-k shortest path routing problem is an extension of finding the shortest path in a given network. Shortest path is one of the most essential measures as it reveals the relations between two nodes in a network. However, in many real world networks, whose diameters are small, top-k shortest path is more interesting as it contains more information about the network topology. Many variations to compute top-k shortest paths have been studied. In this paper, we apply an efficient top-k shortest distance routing algorithm to the link prediction problem and test its efficacy. We compare the results with other base line and state-of-the-art methods as well as with the shortest path. Then, we also propose a top-k distance based graph matching algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=graph%20matching" title="graph matching">graph matching</a>, <a href="https://publications.waset.org/abstracts/search?q=link%20prediction" title=" link prediction"> link prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=shortest%20path" title=" shortest path"> shortest path</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity" title=" similarity"> similarity</a> </p> <a href="https://publications.waset.org/abstracts/63488/top-k-shortest-distance-as-a-similarity-measure" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63488.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">358</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4239</span> Improving Similarity Search Using Clustered Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Deokho%20Kim">Deokho Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Wonwoo%20Lee"> Wonwoo Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Jaewoong%20Lee"> Jaewoong Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Teresa%20Ng"> Teresa Ng</a>, <a href="https://publications.waset.org/abstracts/search?q=Gun-Ill%20Lee"> Gun-Ill Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiwon%20Jeong"> Jiwon Jeong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a method for improving object search accuracy using a deep learning model. A major limitation to provide accurate similarity with deep learning is the requirement of huge amount of data for training pairwise similarity scores (metrics), which is impractical to collect. Thus, similarity scores are usually trained with a relatively small dataset, which comes from a different domain, causing limited accuracy on measuring similarity. For this reason, this paper proposes a deep learning model that can be trained with a significantly small amount of data, a clustered data which of each cluster contains a set of visually similar images. In order to measure similarity distance with the proposed method, visual features of two images are extracted from intermediate layers of a convolutional neural network with various pooling methods, and the network is trained with pairwise similarity scores which is defined zero for images in identical cluster. The proposed method outperforms the state-of-the-art object similarity scoring techniques on evaluation for finding exact items. The proposed method achieves 86.5% of accuracy compared to the accuracy of the state-of-the-art technique, which is 59.9%. That is, an exact item can be found among four retrieved images with an accuracy of 86.5%, and the rest can possibly be similar products more than the accuracy. Therefore, the proposed method can greatly reduce the amount of training data with an order of magnitude as well as providing a reliable similarity metric. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=visual%20search" title="visual search">visual search</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/92185/improving-similarity-search-using-clustered-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92185.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">215</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4238</span> Destination Port Detection For Vessels: An Analytic Tool For Optimizing Port Authorities Resources</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lubna%20Eljabu">Lubna Eljabu</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Etemad"> Mohammad Etemad</a>, <a href="https://publications.waset.org/abstracts/search?q=Stan%20Matwin"> Stan Matwin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Port authorities have many challenges in congested ports to allocate their resources to provide a safe and secure loading/ unloading procedure for cargo vessels. Selecting a destination port is the decision of a vessel master based on many factors such as weather, wavelength and changes of priorities. Having access to a tool which leverages AIS messages to monitor vessel鈥檚 movements and accurately predict their next destination port promotes an effective resource allocation process for port authorities. In this research, we propose a method, namely, Reference Route of Trajectory (RRoT) to assist port authorities in predicting inflow and outflow traffic in their local environment by monitoring Automatic Identification System (AIS) messages. Our RRoT method creates a reference route based on historical AIS messages. It utilizes some of the best trajectory similarity measure to identify the destination of a vessel using their recent movement. We evaluated five different similarity measures such as Discrete Fr麓echet Distance (DFD), Dynamic Time Warping (DTW), Partial Curve Mapping (PCM), Area between two curves (Area) and Curve length (CL). Our experiments show that our method identifies the destination port with an accuracy of 98.97% and an fmeasure of 99.08% using Dynamic Time Warping (DTW) similarity measure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spatial%20temporal%20data%20mining" title="spatial temporal data mining">spatial temporal data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=trajectory%20mining" title=" trajectory mining"> trajectory mining</a>, <a href="https://publications.waset.org/abstracts/search?q=trajectory%20similarity" title=" trajectory similarity"> trajectory similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=resource%20optimization" title=" resource optimization"> resource optimization</a> </p> <a href="https://publications.waset.org/abstracts/137077/destination-port-detection-for-vessels-an-analytic-tool-for-optimizing-port-authorities-resources" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137077.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">121</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4237</span> Text Similarity in Vector Space Models: A Comparative Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omid%20Shahmirzadi">Omid Shahmirzadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Adam%20Lugowski"> Adam Lugowski</a>, <a href="https://publications.waset.org/abstracts/search?q=Kenneth%20Younge"> Kenneth Younge</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Automatic measurement of semantic text similarity is an important task in natural language processing. In this paper, we evaluate the performance of different vector space models to perform this task. We address the real-world problem of modeling patent-to-patent similarity and compare TFIDF (and related extensions), topic models (e.g., latent semantic indexing), and neural models (e.g., paragraph vectors). Contrary to expectations, the added computational cost of text embedding methods is justified only when: 1) the target text is condensed; and 2) the similarity comparison is trivial. Otherwise, TFIDF performs surprisingly well in other cases: in particular for longer and more technical texts or for making finer-grained distinctions between nearest neighbors. Unexpectedly, extensions to the TFIDF method, such as adding noun phrases or calculating term weights incrementally, were not helpful in our context. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=patent" title=" patent"> patent</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20embedding" title=" text embedding"> text embedding</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20similarity" title=" text similarity"> text similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20space%20model" title=" vector space model"> vector space model</a> </p> <a href="https://publications.waset.org/abstracts/102930/text-similarity-in-vector-space-models-a-comparative-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102930.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">175</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4236</span> Discovering the Dimension of Abstractness: Structure-Based Model that Learns New Categories and Categorizes on Different Levels of Abstraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Georgi%20I.%20Petkov">Georgi I. Petkov</a>, <a href="https://publications.waset.org/abstracts/search?q=Ivan%20I.%20Vankov"> Ivan I. Vankov</a>, <a href="https://publications.waset.org/abstracts/search?q=Yolina%20A.%20Petrova"> Yolina A. Petrova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A structure-based model of category learning and categorization at different levels of abstraction is presented. The model compares different structures and expresses their similarity implicitly in the forms of mappings. Based on this similarity, the model can categorize different targets either as members of categories that it already has or creates new categories. The model is novel using two threshold parameters to evaluate the structural correspondence. If the similarity between two structures exceeds the higher threshold, a new sub-ordinate category is created. Vice versa, if the similarity does not exceed the higher threshold but does the lower one, the model creates a new category on higher level of abstraction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analogy-making" title="analogy-making">analogy-making</a>, <a href="https://publications.waset.org/abstracts/search?q=categorization" title=" categorization"> categorization</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20of%20categories" title=" learning of categories"> learning of categories</a>, <a href="https://publications.waset.org/abstracts/search?q=abstraction" title=" abstraction"> abstraction</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20structure" title=" hierarchical structure"> hierarchical structure</a> </p> <a href="https://publications.waset.org/abstracts/94222/discovering-the-dimension-of-abstractness-structure-based-model-that-learns-new-categories-and-categorizes-on-different-levels-of-abstraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94222.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">191</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4235</span> Graph Similarity: Algebraic Model and Its Application to Nonuniform Signal Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nileshkumar%20Vishnav">Nileshkumar Vishnav</a>, <a href="https://publications.waset.org/abstracts/search?q=Aditya%20Tatu"> Aditya Tatu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A recent approach of representing graph signals and graph filters as polynomials is useful for graph signal processing. In this approach, the adjacency matrix plays pivotal role; instead of the more common approach involving graph-Laplacian. In this work, we follow the adjacency matrix based approach and corresponding algebraic signal model. We further expand the theory and introduce the concept of similarity of two graphs. The similarity of graphs is useful in that key properties (such as filter-response, algebra related to graph) get transferred from one graph to another. We demonstrate potential applications of the relation between two similar graphs, such as nonuniform filter design, DTMF detection and signal reconstruction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=graph%20signal%20processing" title="graph signal processing">graph signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=algebraic%20signal%20processing" title=" algebraic signal processing"> algebraic signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20similarity" title=" graph similarity"> graph similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=isospectral%20graphs" title=" isospectral graphs"> isospectral graphs</a>, <a href="https://publications.waset.org/abstracts/search?q=nonuniform%20signal%20processing" title=" nonuniform signal processing"> nonuniform signal processing</a> </p> <a href="https://publications.waset.org/abstracts/59404/graph-similarity-algebraic-model-and-its-application-to-nonuniform-signal-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59404.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">352</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4234</span> Map Matching Performance under Various Similarity Metrics for Heterogeneous Robot Teams</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20C.%20Akay">M. C. Akay</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Aybakan"> A. Aybakan</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Temeltas"> H. Temeltas </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aerial and ground robots have various advantages of usage in different missions. Aerial robots can move quickly and get a different sight of view of the area, but those vehicles cannot carry heavy payloads. On the other hand, unmanned ground vehicles (UGVs) are slow moving vehicles, since those can carry heavier payloads than unmanned aerial vehicles (UAVs). In this context, we investigate the performances of various Similarity Metrics to provide a common map for Heterogeneous Robot Team (HRT) in complex environments. Within the usage of Lidar Odometry and Octree Mapping technique, the local 3D maps of the environment are gathered.  In order to obtain a common map for HRT, informative theoretic similarity metrics are exploited. All types of these similarity metrics gave adequate as allowable simulation time and accurate results that can be used in different types of applications. For the heterogeneous multi robot team, those methods can be used to match different types of maps. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=common%20maps" title="common maps">common maps</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20robot%20team" title=" heterogeneous robot team"> heterogeneous robot team</a>, <a href="https://publications.waset.org/abstracts/search?q=map%20matching" title=" map matching"> map matching</a>, <a href="https://publications.waset.org/abstracts/search?q=informative%20theoretic%20similarity%20metrics" title=" informative theoretic similarity metrics"> informative theoretic similarity metrics</a> </p> <a href="https://publications.waset.org/abstracts/99098/map-matching-performance-under-various-similarity-metrics-for-heterogeneous-robot-teams" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99098.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">168</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4233</span> A Similarity/Dissimilarity Measure to Biological Sequence Alignment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20A.%20Khan">Muhammad A. Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Waseem%20Shahzad"> Waseem Shahzad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Analysis of protein sequences is carried out for the purpose to discover their structural and ancestry relationship. Sequence similarity determines similar protein structures, similar function, and homology detection. Biological sequences composed of amino acid residues or nucleotides provide significant information through sequence alignment. In this paper, we present a new similarity/dissimilarity measure to sequence alignment based on the primary structure of a protein. The approach finds the distance between the two given sequences using the novel sequence alignment algorithm and a mathematical model. The algorithm runs at a time complexity of O(n虏). A distance matrix is generated to construct a phylogenetic tree of different species. The new similarity/dissimilarity measure outperforms other existing methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=alignment" title="alignment">alignment</a>, <a href="https://publications.waset.org/abstracts/search?q=distance" title=" distance"> distance</a>, <a href="https://publications.waset.org/abstracts/search?q=homology" title=" homology"> homology</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20model" title=" mathematical model"> mathematical model</a>, <a href="https://publications.waset.org/abstracts/search?q=phylogenetic%20tree" title=" phylogenetic tree"> phylogenetic tree</a> </p> <a href="https://publications.waset.org/abstracts/95183/a-similaritydissimilarity-measure-to-biological-sequence-alignment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95183.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">178</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4232</span> Operational Measures for Greenhouse Gas Reduction from Ships</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gorana%20Jelic%20Mrcelic">Gorana Jelic Mrcelic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to reduce greenhouse gas emissions from ships, technical and operational measures can be used. Operational measures are easier and cheaper compared to technical measures, so are well recommended. One of the most cost-effective operational measure is fuel consumption. Fuel consumption can be reduced by various options but it sometimes needs investments in new equipment, new procedures and crew education. In order to implement operational measures in everyday procedures and routines on board, good understanding of the mechanisms by which these measures work is essential for the seamen. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=green%20shipping" title="green shipping">green shipping</a>, <a href="https://publications.waset.org/abstracts/search?q=gas%20emission%20reduction" title=" gas emission reduction"> gas emission reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=operational%20measures" title=" operational measures"> operational measures</a>, <a href="https://publications.waset.org/abstracts/search?q=seamen" title=" seamen"> seamen</a> </p> <a href="https://publications.waset.org/abstracts/20223/operational-measures-for-greenhouse-gas-reduction-from-ships" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20223.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">516</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4231</span> 3D Objects Indexing Using Spherical Harmonic for Optimum Measurement Similarity </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Hellam">S. Hellam</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Oulahrir"> Y. Oulahrir</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20El%20Mounchid"> F. El Mounchid</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Sadiq"> A. Sadiq</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Mbarki"> S. Mbarki</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a method for three-dimensional (3-D)-model indexing based on defining a new descriptor, which we call new descriptor using spherical harmonics. The purpose of the method is to minimize, the processing time on the database of objects models and the searching time of similar objects to request object. Firstly we start by defining the new descriptor using a new division of 3-D object in a sphere. Then we define a new distance which will be used in the search for similar objects in the database. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20indexation" title="3D indexation">3D indexation</a>, <a href="https://publications.waset.org/abstracts/search?q=spherical%20harmonic" title=" spherical harmonic"> spherical harmonic</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20of%203D%20objects" title=" similarity of 3D objects"> similarity of 3D objects</a>, <a href="https://publications.waset.org/abstracts/search?q=measurement%20similarity" title=" measurement similarity"> measurement similarity</a> </p> <a href="https://publications.waset.org/abstracts/14277/3d-objects-indexing-using-spherical-harmonic-for-optimum-measurement-similarity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14277.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">433</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4230</span> Analytical Similarity Assessment of Bevacizumab Biosimilar Candidate MB02 Using Multiple State-of-the-Art Assays</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marie-Elise%20Beydon">Marie-Elise Beydon</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Sacristan"> Daniel Sacristan</a>, <a href="https://publications.waset.org/abstracts/search?q=Isabel%20Ruppen"> Isabel Ruppen </a> </p> <p class="card-text"><strong>Abstract:</strong></p> MB02 (Alymsys庐) is a candidate biosimilar to bevacizumab, which was developed against the reference product (RP) Avastin庐 sourced from both the European Union (EU) and United States (US). MB02 has been extensively characterized comparatively to Avastin庐 at a physicochemical and biological level using sensitive orthogonal state-of-the-art analytical methods. MB02 has been demonstrated similar to the RP with regard to its primary and higher-order structure, post- and co-translational profiles such as glycosylation, charge, and size variants. Specific focus has been put on the characterization of Fab-related activities, such as binding to VEGF A 165, which directly reflect the bevacizumab mechanism of action. Fc-related functionality was also investigated, including binding to FcRn, which is indicative of antibodies' half-life. The data generated during the analytical similarity assessment demonstrate the high analytical similarity of MB02 to its RP. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analytical%20similarity" title="analytical similarity">analytical similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=bevacizumab" title=" bevacizumab"> bevacizumab</a>, <a href="https://publications.waset.org/abstracts/search?q=biosimilar" title=" biosimilar"> biosimilar</a>, <a href="https://publications.waset.org/abstracts/search?q=MB02" title=" MB02"> MB02</a> </p> <a href="https://publications.waset.org/abstracts/132954/analytical-similarity-assessment-of-bevacizumab-biosimilar-candidate-mb02-using-multiple-state-of-the-art-assays" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132954.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 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