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indexing</title> <meta name="description" content="Search results for: Google indexing"> <meta name="keywords" content="Google indexing"> <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="Google indexing"> <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> 578</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Google indexing</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">578</span> Comparison of the H-Index of Researchers of Google Scholar and Scopus</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adian%20Fatchur%20Rochim">Adian Fatchur Rochim</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdul%20Muis"> Abdul Muis</a>, <a href="https://publications.waset.org/abstracts/search?q=Riri%20Fitri%20Sari"> Riri Fitri Sari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> H-index has been widely used as a performance indicator of researchers around the world especially in Indonesia. The Government uses Scopus and Google scholar as indexing references in providing recognition and appreciation. However, those two indexing services yield to different H-index values. For that purpose, this paper evaluates the difference of the H-index from those services. Researchers indexed by Webometrics, are used as reference’s data in this paper. Currently, Webometrics only uses H-index from Google Scholar. This paper observed and compared corresponding researchers’ data from Scopus to get their H-index score. Subsequently, some researchers with huge differences in score are observed in more detail on their paper’s publisher. This paper shows that the H-index of researchers in Google Scholar is approximately 2.45 times of their Scopus H-Index. Most difference exists due to the existence of uncertified publishers, which is considered in Google Scholar but not in Scopus. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Google%20Scholar" title="Google Scholar">Google Scholar</a>, <a href="https://publications.waset.org/abstracts/search?q=H-index" title=" H-index"> H-index</a>, <a href="https://publications.waset.org/abstracts/search?q=Scopus" title=" Scopus"> Scopus</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20indicator" title=" performance indicator"> performance indicator</a> </p> <a href="https://publications.waset.org/abstracts/75572/comparison-of-the-h-index-of-researchers-of-google-scholar-and-scopus" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75572.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">275</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">577</span> How to Perform Proper Indexing?</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Watheq%20Mansour">Watheq Mansour</a>, <a href="https://publications.waset.org/abstracts/search?q=Waleed%20Bin%20Owais"> Waleed Bin Owais</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Basheer%20Kotit"> Mohammad Basheer Kotit</a>, <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Khan"> Khaled Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Efficient query processing is one of the utmost requisites in any business environment to satisfy consumer needs. This paper investigates the various types of indexing models, viz. primary, secondary, and multi-level. The investigation is done under the ambit of various types of queries to which each indexing model performs with efficacy. This study also discusses the inherent advantages and disadvantages of each indexing model and how indexing models can be chosen based on a particular environment. This paper also draws parallels between various indexing models and provides recommendations that would help a Database administrator to zero-in on a particular indexing model attributed to the needs and requirements of the production environment. In addition, to satisfy industry and consumer needs attributed to the colossal data generation nowadays, this study has proposed two novel indexing techniques that can be used to index highly unstructured and structured Big Data with efficacy. The study also briefly discusses some best practices that the industry should follow in order to choose an indexing model that is apposite to their prerequisites and requirements. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=indexing" title="indexing">indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=hashing" title=" hashing"> hashing</a>, <a href="https://publications.waset.org/abstracts/search?q=latent%20semantic%20indexing" title=" latent semantic indexing"> latent semantic indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=B-tree" title=" B-tree"> B-tree</a> </p> <a href="https://publications.waset.org/abstracts/134844/how-to-perform-proper-indexing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134844.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">156</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">576</span> Enhancement of Indexing Model for Heterogeneous Multimedia Documents: User Profile Based Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aicha%20Aggoune">Aicha Aggoune</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelkrim%20Bouramoul"> Abdelkrim Bouramoul</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Khiereddine%20Kholladi"> Mohamed Khiereddine Kholladi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recent research shows that user profile as important element can improve heterogeneous information retrieval with its content. In this context, we present our indexing model for heterogeneous multimedia documents. This model is based on the combination of user profile to the indexing process. The general idea of our proposal is to operate the common concepts between the representation of a document and the definition of a user through his profile. These two elements will be added as additional indexing entities to enrich the heterogeneous corpus documents indexes. We have developed IRONTO domain ontology allowing annotation of documents. We will present also the developed tool validating the proposed model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=indexing%20model" title="indexing model">indexing model</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20profile" title=" user profile"> user profile</a>, <a href="https://publications.waset.org/abstracts/search?q=multimedia%20document" title=" multimedia document"> multimedia document</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20of%20sources" title=" heterogeneous of sources"> heterogeneous of sources</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology" title=" ontology"> ontology</a> </p> <a href="https://publications.waset.org/abstracts/41159/enhancement-of-indexing-model-for-heterogeneous-multimedia-documents-user-profile-based-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41159.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">348</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">575</span> Lecture Video Indexing and Retrieval Using Topic Keywords</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20J.%20Sandesh">B. J. Sandesh</a>, <a href="https://publications.waset.org/abstracts/search?q=Saurabha%20Jirgi"> Saurabha Jirgi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Vidya"> S. Vidya</a>, <a href="https://publications.waset.org/abstracts/search?q=Prakash%20Eljer"> Prakash Eljer</a>, <a href="https://publications.waset.org/abstracts/search?q=Gowri%20Srinivasa"> Gowri Srinivasa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a framework to help users to search and retrieve the portions in the lecture video of their interest. This is achieved by temporally segmenting and indexing the lecture video using the topic keywords. We use transcribed text from the video and documents relevant to the video topic extracted from the web for this purpose. The keywords for indexing are found by applying the non-negative matrix factorization (NMF) topic modeling techniques on the web documents. Our proposed technique first creates indices on the transcribed documents using the topic keywords, and these are mapped to the video to find the start and end time of the portions of the video for a particular topic. This time information is stored in the index table along with the topic keyword which is used to retrieve the specific portions of the video for the query provided by the users. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20indexing%20and%20retrieval" title="video indexing and retrieval">video indexing and retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=lecture%20videos" title=" lecture videos"> lecture videos</a>, <a href="https://publications.waset.org/abstracts/search?q=content%20based%20video%20search" title=" content based video search"> content based video search</a>, <a href="https://publications.waset.org/abstracts/search?q=multimodal%20indexing" title=" multimodal indexing"> multimodal indexing</a> </p> <a href="https://publications.waset.org/abstracts/77066/lecture-video-indexing-and-retrieval-using-topic-keywords" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77066.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">250</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">574</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’s 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’s 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’s 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">573</span> Blended Learning through Google Classroom</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lee%20Bih%20Ni">Lee Bih Ni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses that good learning involves all academic groups in the school. Blended learning is learning outside the classroom. Google Classroom is a free service learning app for schools, non-profit organizations and anyone with a personal Google account. Facilities accessed through computers and mobile phones are very useful for school teachers and students. Blended learning classrooms using both traditional and technology-based methods for teaching have become the norm for many educators. Using Google Classroom gives students access to online learning. Even if the teacher is not in the classroom, the teacher can provide learning. This is the supervision of the form of the teacher when the student is outside the school. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blended%20learning" title="blended learning">blended learning</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20app" title=" learning app"> learning app</a>, <a href="https://publications.waset.org/abstracts/search?q=google%20classroom" title=" google classroom"> google classroom</a>, <a href="https://publications.waset.org/abstracts/search?q=schools" title=" schools"> schools</a> </p> <a href="https://publications.waset.org/abstracts/108493/blended-learning-through-google-classroom" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108493.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">146</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">572</span> Semantic Indexing Improvement for Textual Documents: Contribution of Classification by Fuzzy Association Rules</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Maraoui">Mohsen Maraoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the aim of natural language processing applications improvement, such as information retrieval, machine translation, lexical disambiguation, we focus on statistical approach to semantic indexing for multilingual text documents based on conceptual network formalism. We propose to use this formalism as an indexing language to represent the descriptive concepts and their weighting. These concepts represent the content of the document. Our contribution is based on two steps. In the first step, we propose the extraction of index terms using the multilingual lexical resource Euro WordNet (EWN). In the second step, we pass from the representation of index terms to the representation of index concepts through conceptual network formalism. This network is generated using the EWN resource and pass by a classification step based on association rules model (in attempt to discover the non-taxonomic relations or contextual relations between the concepts of a document). These relations are latent relations buried in the text and carried by the semantic context of the co-occurrence of concepts in the document. Our proposed indexing approach can be applied to text documents in various languages because it is based on a linguistic method adapted to the language through a multilingual thesaurus. Next, we apply the same statistical process regardless of the language in order to extract the significant concepts and their associated weights. We prove that the proposed indexing approach provides encouraging results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=concept%20extraction" title="concept extraction">concept extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=conceptual%20network%20formalism" title=" conceptual network formalism"> conceptual network formalism</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20association%20rules" title=" fuzzy association rules"> fuzzy association rules</a>, <a href="https://publications.waset.org/abstracts/search?q=multilingual%20thesaurus" title=" multilingual thesaurus"> multilingual thesaurus</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20indexing" title=" semantic indexing"> semantic indexing</a> </p> <a href="https://publications.waset.org/abstracts/98854/semantic-indexing-improvement-for-textual-documents-contribution-of-classification-by-fuzzy-association-rules" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98854.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">141</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">571</span> Compressed Suffix Arrays to Self-Indexes Based on Partitioned Elias-Fano</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guo%20Wenyu">Guo Wenyu</a>, <a href="https://publications.waset.org/abstracts/search?q=Qu%20Youli"> Qu Youli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A practical and simple self-indexing data structure, Partitioned Elias-Fano (PEF) - Compressed Suffix Arrays (CSA), is built in linear time for the CSA based on PEF indexes. Moreover, the PEF-CSA is compared with two classical compressed indexing methods, Ferragina and Manzini implementation (FMI) and Sad-CSA on different type and size files in Pizza & Chili. The PEF-CSA performs better on the existing data in terms of the compression ratio, count, and locates time except for the evenly distributed data such as proteins data. The observations of the experiments are that the distribution of the φ is more important than the alphabet size on the compression ratio. Unevenly distributed data φ makes better compression effect, and the larger the size of the hit counts, the longer the count and locate time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressed%20suffix%20array" title="compressed suffix array">compressed suffix array</a>, <a href="https://publications.waset.org/abstracts/search?q=self-indexing" title=" self-indexing"> self-indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=partitioned%20Elias-Fano" title=" partitioned Elias-Fano"> partitioned Elias-Fano</a>, <a href="https://publications.waset.org/abstracts/search?q=PEF-CSA" title=" PEF-CSA"> PEF-CSA</a> </p> <a href="https://publications.waset.org/abstracts/65986/compressed-suffix-arrays-to-self-indexes-based-on-partitioned-elias-fano" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65986.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">252</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">570</span> Graph Codes - 2D Projections of Multimedia Feature Graphs for Fast and Effective Retrieval</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Stefan%20Wagenpfeil">Stefan Wagenpfeil</a>, <a href="https://publications.waset.org/abstracts/search?q=Felix%20Engel"> Felix Engel</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul%20McKevitt"> Paul McKevitt</a>, <a href="https://publications.waset.org/abstracts/search?q=Matthias%20Hemmje"> Matthias Hemmje</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multimedia Indexing and Retrieval is generally designed and implemented by employing feature graphs. These graphs typically contain a significant number of nodes and edges to reflect the level of detail in feature detection. A higher level of detail increases the effectiveness of the results but also leads to more complex graph structures. However, graph-traversal-based algorithms for similarity are quite inefficient and computation intensive, especially for large data structures. To deliver fast and effective retrieval, an efficient similarity algorithm, particularly for large graphs, is mandatory. Hence, in this paper, we define a graph-projection into a 2D space (Graph Code) as well as the corresponding algorithms for indexing and retrieval. We show that calculations in this space can be performed more efficiently than graph-traversals due to a simpler processing model and a high level of parallelization. In consequence, we prove that the effectiveness of retrieval also increases substantially, as Graph Codes facilitate more levels of detail in feature fusion. Thus, Graph Codes provide a significant increase in efficiency and effectiveness (especially for Multimedia indexing and retrieval) and can be applied to images, videos, audio, and text information. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=indexing" title="indexing">indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=retrieval" title=" retrieval"> retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=multimedia" title=" multimedia"> multimedia</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20algorithm" title=" graph algorithm"> graph algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20code" title=" graph code"> graph code</a> </p> <a href="https://publications.waset.org/abstracts/135289/graph-codes-2d-projections-of-multimedia-feature-graphs-for-fast-and-effective-retrieval" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135289.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">161</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">569</span> Utilizing Google Earth for Internet GIS</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alireza%20Derambakhsh">Alireza Derambakhsh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objective of this examination is to explore the capability of utilizing Google Earth for Internet GIS applications. The study particularly analyzes the utilization of vector and characteristic information and the capability of showing and preparing this information in new ways utilizing the Google Earth stage. It has progressively been perceived that future improvements in GIS will fixate on Internet GIS, and in three noteworthy territories: GIS information access, spatial data scattering and GIS displaying/preparing. Google Earth is one of the group of geobrowsers that offer a free and simple to utilize administration that empower information with a spatial part to be overlain on top of a 3-D model of the Earth. This examination makes a methodological structure to accomplish its objective that comprises of three noteworthy parts: A database level, an application level and a customer level. As verification of idea a web model has been produced, which incorporates a differing scope of datasets and lets clients direst inquiries and make perceptions of this custom information. The outcomes uncovered that both vector and property information can be successfully spoken to and imagined utilizing Google Earth. In addition, the usefulness to question custom information and envision results has been added to the Google Earth stage. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Google%20earth" title="Google earth">Google earth</a>, <a href="https://publications.waset.org/abstracts/search?q=internet%20GIS" title=" internet GIS"> internet GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=vector" title=" vector"> vector</a>, <a href="https://publications.waset.org/abstracts/search?q=characteristic%20information" title=" characteristic information"> characteristic information</a> </p> <a href="https://publications.waset.org/abstracts/33274/utilizing-google-earth-for-internet-gis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33274.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">308</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">568</span> Design and Implementation of Partial Denoising Boundary Image Matching Using Indexing Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bum-Soo%20Kim">Bum-Soo Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jin-Uk%20Kim"> Jin-Uk Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we design and implement a partial denoising boundary image matching system using indexing techniques. Converting boundary images to time-series makes it feasible to perform fast search using indexes even on a very large image database. Thus, using this converting method we develop a client-server system based on the previous partial denoising research in the GUI (graphical user interface) environment. The client first converts a query image given by a user to a time-series and sends denoising parameters and the tolerance with this time-series to the server. The server identifies similar images from the index by evaluating a range query, which is constructed using inputs given from the client, and sends the resulting images to the client. Experimental results show that our system provides much intuitive and accurate matching result. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=boundary%20image%20matching" title="boundary image matching">boundary image matching</a>, <a href="https://publications.waset.org/abstracts/search?q=indexing" title=" indexing"> indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20denoising" title=" partial denoising"> partial denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=time-series%20matching" title=" time-series matching"> time-series matching</a> </p> <a href="https://publications.waset.org/abstracts/97170/design-and-implementation-of-partial-denoising-boundary-image-matching-using-indexing-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97170.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">138</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">567</span> Post Pandemic Mobility Analysis through Indexing and Sharding in MongoDB: Performance Optimization and Insights</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karan%20Vishavjit">Karan Vishavjit</a>, <a href="https://publications.waset.org/abstracts/search?q=Aakash%20Lakra"> Aakash Lakra</a>, <a href="https://publications.waset.org/abstracts/search?q=Shafaq%20Khan"> Shafaq Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The COVID-19 pandemic has pushed healthcare professionals to use big data analytics as a vital tool for tracking and evaluating the effects of contagious viruses. To effectively analyze huge datasets, efficient NoSQL databases are needed. The analysis of post-COVID-19 health and well-being outcomes and the evaluation of the effectiveness of government efforts during the pandemic is made possible by this research’s integration of several datasets, which cuts down on query processing time and creates predictive visual artifacts. We recommend applying sharding and indexing technologies to improve query effectiveness and scalability as the dataset expands. Effective data retrieval and analysis are made possible by spreading the datasets into a sharded database and doing indexing on individual shards. Analysis of connections between governmental activities, poverty levels, and post-pandemic well being is the key goal. We want to evaluate the effectiveness of governmental initiatives to improve health and lower poverty levels. We will do this by utilising advanced data analysis and visualisations. The findings provide relevant data that supports the advancement of UN sustainable objectives, future pandemic preparation, and evidence-based decision-making. This study shows how Big Data and NoSQL databases may be used to address problems with global health. <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=COVID-19" title=" COVID-19"> COVID-19</a>, <a href="https://publications.waset.org/abstracts/search?q=health" title=" health"> health</a>, <a href="https://publications.waset.org/abstracts/search?q=indexing" title=" indexing"> indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=NoSQL" title=" NoSQL"> NoSQL</a>, <a href="https://publications.waset.org/abstracts/search?q=sharding" title=" sharding"> sharding</a>, <a href="https://publications.waset.org/abstracts/search?q=scalability" title=" scalability"> scalability</a>, <a href="https://publications.waset.org/abstracts/search?q=well%20being" title=" well being"> well being</a> </p> <a href="https://publications.waset.org/abstracts/172020/post-pandemic-mobility-analysis-through-indexing-and-sharding-in-mongodb-performance-optimization-and-insights" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172020.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">70</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">566</span> The Pedagogical Functions of Arts and Cultural-Heritage Education with ICTs in Museums – A Case Study of FINNA and Google Art </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pei%20Zhao">Pei Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Sara%20Sintonen"> Sara Sintonen</a>, <a href="https://publications.waset.org/abstracts/search?q=Heikki%20Kyn%C3%A4slahti"> Heikki Kynäslahti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Digital museums and arts galleries have become popular in museum education and management. Museum and arts galleries website is one of the most effective and efficient ways. Google, a corporation specializing in Internet-related services and projects, not only puts high-resolution arts images online, but also uses augmented-reality in digital art gallery. The Google Art Project, Google’s production, provides users a platform in appreciating and learning arts. After Google Art Project, more and more countries released their own museum and arts gallery websites, like British Paining in BBC, and FINNA in Finland. Pedagogical function in these websites is one of the most important functions. In this paper, we use Google Art Project and FINNA as the case studies to investigate what kinds of pedagogical functions exist in these websites. Finally, this paper will give the recommendation to digital museums and websites development, especially the pedagogical functions development, in the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arts%20education" title="arts education">arts education</a>, <a href="https://publications.waset.org/abstracts/search?q=cultural-heritage%20education" title=" cultural-heritage education"> cultural-heritage education</a>, <a href="https://publications.waset.org/abstracts/search?q=education%20with%20ICTs" title=" education with ICTs"> education with ICTs</a>, <a href="https://publications.waset.org/abstracts/search?q=pedagogical%20functions" title=" pedagogical functions"> pedagogical functions</a> </p> <a href="https://publications.waset.org/abstracts/24955/the-pedagogical-functions-of-arts-and-cultural-heritage-education-with-icts-in-museums-a-case-study-of-finna-and-google-art" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24955.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">548</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">565</span> Topic Modelling Using Latent Dirichlet Allocation and Latent Semantic Indexing on SA Telco Twitter Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Phumelele%20Kubheka">Phumelele Kubheka</a>, <a href="https://publications.waset.org/abstracts/search?q=Pius%20Owolawi"> Pius Owolawi</a>, <a href="https://publications.waset.org/abstracts/search?q=Gbolahan%20Aiyetoro"> Gbolahan Aiyetoro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Twitter is one of the most popular social media platforms where users can share their opinions on different subjects. As of 2010, The Twitter platform generates more than 12 Terabytes of data daily, ~ 4.3 petabytes in a single year. For this reason, Twitter is a great source for big mining data. Many industries such as Telecommunication companies can leverage the availability of Twitter data to better understand their markets and make an appropriate business decision. This study performs topic modeling on Twitter data using Latent Dirichlet Allocation (LDA). The obtained results are benchmarked with another topic modeling technique, Latent Semantic Indexing (LSI). The study aims to retrieve topics on a Twitter dataset containing user tweets on South African Telcos. Results from this study show that LSI is much faster than LDA. However, LDA yields better results with higher topic coherence by 8% for the best-performing model represented in Table 1. A higher topic coherence score indicates better performance of the model. <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=latent%20Dirichlet%20allocation" title=" latent Dirichlet allocation"> latent Dirichlet allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=latent%20semantic%20indexing" title=" latent semantic indexing"> latent semantic indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=telco" title=" telco"> telco</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20modeling" title=" topic modeling"> topic modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=twitter" title=" twitter"> twitter</a> </p> <a href="https://publications.waset.org/abstracts/147818/topic-modelling-using-latent-dirichlet-allocation-and-latent-semantic-indexing-on-sa-telco-twitter-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147818.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">150</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">564</span> Estimating Current Suicide Rates Using Google Trends</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ladislav%20Kristoufek">Ladislav Kristoufek</a>, <a href="https://publications.waset.org/abstracts/search?q=Helen%20Susannah%20Moat"> Helen Susannah Moat</a>, <a href="https://publications.waset.org/abstracts/search?q=Tobias%20Preis"> Tobias Preis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data on the number of people who have committed suicide tends to be reported with a substantial time lag of around two years. We examine whether online activity measured by Google searches can help us improve estimates of the number of suicide occurrences in England before official figures are released. Specifically, we analyse how data on the number of Google searches for the terms “depression” and “suicide” relate to the number of suicides between 2004 and 2013. We find that estimates drawing on Google data are significantly better than estimates using previous suicide data alone. We show that a greater number of searches for the term “depression” is related to fewer suicides, whereas a greater number of searches for the term “suicide” is related to more suicides. Data on suicide related search behaviour can be used to improve current estimates of the number of suicide occurrences. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nowcasting" title="nowcasting">nowcasting</a>, <a href="https://publications.waset.org/abstracts/search?q=search%20data" title=" search data"> search data</a>, <a href="https://publications.waset.org/abstracts/search?q=Google%20Trends" title=" Google Trends"> Google Trends</a>, <a href="https://publications.waset.org/abstracts/search?q=official%20statistics" title=" official statistics"> official statistics</a> </p> <a href="https://publications.waset.org/abstracts/59622/estimating-current-suicide-rates-using-google-trends" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59622.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">357</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">563</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">562</span> Extraction of Text Subtitles in Multimedia Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amarjit%20Singh">Amarjit Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a method for extraction of text subtitles in large video is proposed. The video data needs to be annotated for many multimedia applications. Text is incorporated in digital video for the motive of providing useful information about that video. So need arises to detect text present in video to understanding and video indexing. This is achieved in two steps. First step is text localization and the second step is text verification. The method of text detection can be extended to text recognition which finds applications in automatic video indexing; video annotation and content based video retrieval. The method has been tested on various types of videos. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video" title="video">video</a>, <a href="https://publications.waset.org/abstracts/search?q=subtitles" title=" subtitles"> subtitles</a>, <a href="https://publications.waset.org/abstracts/search?q=extraction" title=" extraction"> extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=annotation" title=" annotation"> annotation</a>, <a href="https://publications.waset.org/abstracts/search?q=frames" title=" frames"> frames</a> </p> <a href="https://publications.waset.org/abstracts/24441/extraction-of-text-subtitles-in-multimedia-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24441.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">601</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">561</span> Mountain Photo Sphere: An Android Application of Mountain Hiking Street View</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yanto%20Budisusanto">Yanto Budisusanto</a>, <a href="https://publications.waset.org/abstracts/search?q=Aulia%20Rachmawati"> Aulia Rachmawati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Land navigation technology that is being developed is Google Street View to provide 360° street views, enabling the user to know the road conditions physically with the photo display. For climbers, especially beginners, detail information of climbing terrain is needed so climbers can prepare supplies and strategies before climbing. Therefore, we built a mountaineer guide application named Mountain Photo Sphere. This application displays a 360̊ panoramic view of mountain hiking trail and important points along the hiking path and its surrounding conditions. By combining panoramic photos 360̊ and tracking paths from coordinate data, a virtual tour will be formed. It is built using Java language and Android Studio. The hiking trail map composed by Google Maps API (Gaining access to google maps), Google GEO API (Gaining access to google maps), and OpenStreetMap API (Getting map files to be accessed offline on the Application). This application can be accessed offline so that climbers can use the application during climbing activities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=google%20street%20view" title="google street view">google street view</a>, <a href="https://publications.waset.org/abstracts/search?q=panoramic%20photo%20360%C2%B0" title=" panoramic photo 360°"> panoramic photo 360°</a>, <a href="https://publications.waset.org/abstracts/search?q=mountain%20hiking" title=" mountain hiking"> mountain hiking</a>, <a href="https://publications.waset.org/abstracts/search?q=mountain%20photo%20sphere" title=" mountain photo sphere"> mountain photo sphere</a> </p> <a href="https://publications.waset.org/abstracts/105002/mountain-photo-sphere-an-android-application-of-mountain-hiking-street-view" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105002.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">166</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">560</span> Google Translate: AI Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shaima%20Almalhan">Shaima Almalhan</a>, <a href="https://publications.waset.org/abstracts/search?q=Lubna%20Shukri"> Lubna Shukri</a>, <a href="https://publications.waset.org/abstracts/search?q=Miriam%20Talal"> Miriam Talal</a>, <a href="https://publications.waset.org/abstracts/search?q=Safaa%20Teskieh"> Safaa Teskieh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Since artificial intelligence is a rapidly evolving topic that has had a significant impact on technical growth and innovation, this paper examines people's awareness, use, and engagement with the Google Translate application. To see how familiar aware users are with the app and its features, quantitative and qualitative research was conducted. The findings revealed that consumers have a high level of confidence in the application and how far people they benefit from this sort of innovation and how convenient it makes communication. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=google%20translate" title=" google translate"> google translate</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20recognition" title=" speech recognition"> speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20translation" title=" language translation"> language translation</a>, <a href="https://publications.waset.org/abstracts/search?q=camera%20%20translation" title=" camera translation"> camera translation</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20to%20text" title=" speech to text"> speech to text</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20to%20speech" title=" text to speech"> text to speech</a> </p> <a href="https://publications.waset.org/abstracts/145090/google-translate-ai-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145090.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">154</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">559</span> The Democratization of 3D Capturing: An Application Investigating Google Tango Potentials</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carlo%20Bianchini">Carlo Bianchini</a>, <a href="https://publications.waset.org/abstracts/search?q=Lorenzo%20Catena"> Lorenzo Catena</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The appearance of 3D scanners and then, more recently, of image-based systems that generate point clouds directly from common digital images have deeply affected the survey process in terms of both capturing and 2D/3D modelling. In this context, low cost and mobile systems are increasingly playing a key role and actually paving the way to the democratization of what in the past was the realm of few specialized technicians and expensive equipment. The application of Google Tango on the ancient church of Santa Maria delle Vigne in Pratica di Mare – Rome presented in this paper is one of these examples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=the%20architectural%20survey" title="the architectural survey">the architectural survey</a>, <a href="https://publications.waset.org/abstracts/search?q=augmented%2Fmixed%2Fvirtual%20reality" title=" augmented/mixed/virtual reality"> augmented/mixed/virtual reality</a>, <a href="https://publications.waset.org/abstracts/search?q=Google%20Tango%20project" title=" Google Tango project"> Google Tango project</a>, <a href="https://publications.waset.org/abstracts/search?q=image-based%203D%20capturing" title=" image-based 3D capturing"> image-based 3D capturing</a> </p> <a href="https://publications.waset.org/abstracts/91863/the-democratization-of-3d-capturing-an-application-investigating-google-tango-potentials" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91863.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">149</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">558</span> The Potential of Cloud Computing in Overcoming the Problems of Collective Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hussah%20M.%20AlShayea">Hussah M. AlShayea</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aimed to identify the potential of cloud computing, "Google Drive" in overcoming the problems of collective learning from the viewpoint of Princess Noura University students. The study included (92) students from the College of Education. To achieve the goal of the study, several steps have been taken. First, the most important problems of collective learning were identified from the viewpoint of the students. After that, a survey identifying the potential of cloud computing "Google Drive" in overcoming the problems of collective learning was distributed among the students. The study results showed that the students believe that the use of Google Drive contributed to overcoming these problems. In the light of those results, the researcher presented a set of recommendations and proposals, including: encouraging teachers and learners to employ cloud computing to overcome the problems and constraints of collective learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title="cloud computing">cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=collective%20learning" title=" collective learning"> collective learning</a>, <a href="https://publications.waset.org/abstracts/search?q=Google%20drive" title=" Google drive"> Google drive</a>, <a href="https://publications.waset.org/abstracts/search?q=Princess%20Noura%20University" title=" Princess Noura University"> Princess Noura University</a> </p> <a href="https://publications.waset.org/abstracts/16964/the-potential-of-cloud-computing-in-overcoming-the-problems-of-collective-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16964.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">492</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">557</span> Comparison of Slope Data between Google Earth and the Digital Terrain Model, for Registration in Car</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andr%C3%A9%20Felipe%20Gimenez">André Felipe Gimenez</a>, <a href="https://publications.waset.org/abstracts/search?q=Fl%C3%A1via%20Alessandra%20Ribeiro%20da%20Silva"> Flávia Alessandra Ribeiro da Silva</a>, <a href="https://publications.waset.org/abstracts/search?q=Roberto%20Saverio%20Souza%20Costa"> Roberto Saverio Souza Costa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Currently, the rural producer has been facing problems regarding environmental regularization, which is precisely why the CAR (Rural Environmental Registry) was created. CAR is an electronic registry for rural properties with the purpose of assimilating notions about legal reserve areas, permanent preservation areas, areas of limited use, stable areas, forests and remnants of native vegetation, and all rural properties in Brazil. . The objective of this work was to evaluate and compare altimetry and slope data from google Earth with a digital terrain model (MDT) generated by aerophotogrammetry, in three plots of a steep slope, for the purpose of declaration in the CAR (Rural Environmental Registry). The realization of this work is justified in these areas, in which rural landowners have doubts about the reliability of the use of the free software Google Earth to diagnose inclinations greater than 25 degrees, as recommended by federal law 12651/2012. Added to the fact that in the literature, there is a deficiency of this type of study for the purpose of declaration of the CAR. The results showed that when comparing the drone altimetry data with the Google Earth image data, in areas of high slope (above 40% slope), Google underestimated the real values of terrain slope. Thus, it is concluded that Google Earth is not reliable for diagnosing areas with an inclination greater than 25 degrees (46% declivity) for the purpose of declaration in the CAR, being essential to carry out the local topographic survey. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MDT" title="MDT">MDT</a>, <a href="https://publications.waset.org/abstracts/search?q=drone" title=" drone"> drone</a>, <a href="https://publications.waset.org/abstracts/search?q=RPA" title=" RPA"> RPA</a>, <a href="https://publications.waset.org/abstracts/search?q=SiCar" title=" SiCar"> SiCar</a>, <a href="https://publications.waset.org/abstracts/search?q=photogrammetry" title=" photogrammetry"> photogrammetry</a> </p> <a href="https://publications.waset.org/abstracts/152861/comparison-of-slope-data-between-google-earth-and-the-digital-terrain-model-for-registration-in-car" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152861.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">556</span> 3D Object Retrieval Based on Similarity Calculation in 3D Computer Aided Design Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Fradi">Ahmed Fradi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, recent technological advances in the acquisition, modeling, and processing of three-dimensional (3D) objects data lead to the creation of models stored in huge databases, which are used in various domains such as computer vision, augmented reality, game industry, medicine, CAD (Computer-aided design), 3D printing etc. On the other hand, the industry is currently benefiting from powerful modeling tools enabling designers to easily and quickly produce 3D models. The great ease of acquisition and modeling of 3D objects make possible to create large 3D models databases, then, it becomes difficult to navigate them. Therefore, the indexing of 3D objects appears as a necessary and promising solution to manage this type of data, to extract model information, retrieve an existing model or calculate similarity between 3D objects. The objective of the proposed research is to develop a framework allowing easy and fast access to 3D objects in a CAD models database with specific indexing algorithm to find objects similar to a reference model. Our main objectives are to study existing methods of similarity calculation of 3D objects (essentially shape-based methods) by specifying the characteristics of each method as well as the difference between them, and then we will propose a new approach for indexing and comparing 3D models, which is suitable for our case study and which is based on some previously studied methods. Our proposed approach is finally illustrated by an implementation, and evaluated in a professional context. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CAD" title="CAD">CAD</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20object%20retrieval" title=" 3D object retrieval"> 3D object retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=shape%20based%20retrieval" title=" shape based retrieval"> shape based retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20calculation" title=" similarity calculation"> similarity calculation</a> </p> <a href="https://publications.waset.org/abstracts/78341/3d-object-retrieval-based-on-similarity-calculation-in-3d-computer-aided-design-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78341.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">262</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">555</span> An Online Adaptive Thresholding Method to Classify Google Trends Data Anomalies for Investor Sentiment Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Duygu%20Dere">Duygu Dere</a>, <a href="https://publications.waset.org/abstracts/search?q=Mert%20Ergeneci"> Mert Ergeneci</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaan%20Gokcesu"> Kaan Gokcesu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Google Trends data has gained increasing popularity in the applications of behavioral finance, decision science and risk management. Because of Google’s wide range of use, the Trends statistics provide significant information about the investor sentiment and intention, which can be used as decisive factors for corporate and risk management fields. However, an anomaly, a significant increase or decrease, in a certain query cannot be detected by the state of the art applications of computation due to the random baseline noise of the Trends data, which is modelled as an Additive white Gaussian noise (AWGN). Since through time, the baseline noise power shows a gradual change an adaptive thresholding method is required to track and learn the baseline noise for a correct classification. To this end, we introduce an online method to classify meaningful deviations in Google Trends data. Through extensive experiments, we demonstrate that our method can successfully classify various anomalies for plenty of different data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20data%20processing" title="adaptive data processing">adaptive data processing</a>, <a href="https://publications.waset.org/abstracts/search?q=behavioral%20finance" title=" behavioral finance "> behavioral finance </a>, <a href="https://publications.waset.org/abstracts/search?q=convex%20optimization" title=" convex optimization"> convex optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20learning" title=" online learning"> online learning</a>, <a href="https://publications.waset.org/abstracts/search?q=soft%20minimum%20thresholding" title=" soft minimum thresholding"> soft minimum thresholding</a> </p> <a href="https://publications.waset.org/abstracts/92282/an-online-adaptive-thresholding-method-to-classify-google-trends-data-anomalies-for-investor-sentiment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92282.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">167</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">554</span> Optimizing the Use of Google Translate in Translation Teaching: A Case Study at Prince Sultan University </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saadia%20Elamin">Saadia Elamin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The quasi-universal use of smart phones with internet connection available all the time makes it a reflex action for translation undergraduates, once they encounter the least translation problem, to turn to the freely available web resource: Google Translate. Like for other translator resources and aids, the use of Google Translate needs to be moderated in such a way that it contributes to developing translation competence. Here, instead of interfering with students’ learning by providing ready-made solutions which might not always fit into the contexts of use, it can help to consolidate the skills of analysis and transfer which students have already acquired. One way to do so is by training students to adhere to the basic principles of translation work. The most important of these is that analyzing the source text for comprehension comes first and foremost before jumping into the search for target language equivalents. Another basic principle is that certain translator aids and tools can be used for comprehension, while others are to be confined to the phase of re-expressing the meaning into the target language. The present paper reports on the experience of making a measured and reasonable use of Google Translate in translation teaching at Prince Sultan University (PSU), Riyadh. First, it traces the development that has taken place in the field of translation in this age of information technology, be it in translation teaching and translator training, or in the real-world practice of the profession. Second, it describes how, with the aim of reflecting this development onto the way translation is taught, senior students, after being trained on post-editing machine translation output, are authorized to use Google Translate in classwork and assignments. Third, the paper elaborates on the findings of this case study which has demonstrated that Google Translate, if used at the appropriate levels of training, can help to enhance students’ ability to perform different translation tasks. This help extends from the search for terms and expressions, to the tasks of drafting the target text, revising its content and finally editing it. In addition, using Google Translate in this way fosters a reflexive and critical attitude towards web resources in general, maximizing thus the benefit gained from them in preparing students to meet the requirements of the modern translation job market. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Google%20Translate" title="Google Translate">Google Translate</a>, <a href="https://publications.waset.org/abstracts/search?q=post-editing%20machine%20translation%20output" title=" post-editing machine translation output"> post-editing machine translation output</a>, <a href="https://publications.waset.org/abstracts/search?q=principles%20of%20translation%20work" title=" principles of translation work"> principles of translation work</a>, <a href="https://publications.waset.org/abstracts/search?q=translation%20competence" title=" translation competence"> translation competence</a>, <a href="https://publications.waset.org/abstracts/search?q=translation%20teaching" title=" translation teaching"> translation teaching</a>, <a href="https://publications.waset.org/abstracts/search?q=translator%20aids%20and%20tools" title=" translator aids and tools"> translator aids and tools</a> </p> <a href="https://publications.waset.org/abstracts/60413/optimizing-the-use-of-google-translate-in-translation-teaching-a-case-study-at-prince-sultan-university" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60413.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">473</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">553</span> Activation of Google Classroom Features to Engage Introvert Students in Comprehensible Output</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Raghad%20Dwaik">Raghad Dwaik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is well known in language acquisition literature that a mere understanding of a reading text is not enough to help students build proficiency in comprehension. Students should rather follow understanding by attempting to express what has been understood by pushing their competence to the limit. Learners' attempt to push their competence was given the term "comprehensible output" by Swain (1985). Teachers in large classes, however, find it sometimes difficult to give all students a chance to communicate their views or to share their ideas during the short class time. In most cases, students who are outgoing dominate class discussion and get more opportunities for practice which leads to ignoring the shy students totally while helping the good ones become better. This paper presents the idea of using Google Classroom features of posting and commenting to allow students who hesitate to participate in class discussions about a reading text to write their views on the wall of a Google Classroom and share them later after they have received feedback and comments from classmates. Such attempts lead to developing their proficiency through additional practice in comprehensible output and to enhancing their confidence in themselves and their views. It was found that virtual classroom interaction would help students maintain vocabulary, use more complex structures and focus on meaning besides form. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=learning%20groups" title="learning groups">learning groups</a>, <a href="https://publications.waset.org/abstracts/search?q=reading%20TESOL" title=" reading TESOL"> reading TESOL</a>, <a href="https://publications.waset.org/abstracts/search?q=Google%20Classroom" title=" Google Classroom"> Google Classroom</a>, <a href="https://publications.waset.org/abstracts/search?q=comprehensible%20output" title=" comprehensible output"> comprehensible output</a> </p> <a href="https://publications.waset.org/abstracts/168277/activation-of-google-classroom-features-to-engage-introvert-students-in-comprehensible-output" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168277.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">78</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">552</span> Design and Development of a Platform for Analyzing Spatio-Temporal Data from Wireless Sensor Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Walid%20Fantazi">Walid Fantazi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The development of sensor technology (such as microelectromechanical systems (MEMS), wireless communications, embedded systems, distributed processing and wireless sensor applications) has contributed to a broad range of WSN applications which are capable of collecting a large amount of spatiotemporal data in real time. These systems require real-time data processing to manage storage in real time and query the data they process. In order to cover these needs, we propose in this paper a Snapshot spatiotemporal data model based on object-oriented concepts. This model allows saving storing and reducing data redundancy which makes it easier to execute spatiotemporal queries and save analyzes time. Further, to ensure the robustness of the system as well as the elimination of congestion from the main access memory we propose a spatiotemporal indexing technique in RAM called Captree *. As a result, we offer an RIA (Rich Internet Application) -based SOA application architecture which allows the remote monitoring and control. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=WSN" title="WSN">WSN</a>, <a href="https://publications.waset.org/abstracts/search?q=indexing%20data" title=" indexing data"> indexing data</a>, <a href="https://publications.waset.org/abstracts/search?q=SOA" title=" SOA"> SOA</a>, <a href="https://publications.waset.org/abstracts/search?q=RIA" title=" RIA"> RIA</a>, <a href="https://publications.waset.org/abstracts/search?q=geographic%20information%20system" title=" geographic information system "> geographic information system </a> </p> <a href="https://publications.waset.org/abstracts/88946/design-and-development-of-a-platform-for-analyzing-spatio-temporal-data-from-wireless-sensor-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88946.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">254</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">551</span> State of Play of Mobile Government Apps on Google Play Store</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdelbaset%20Rabaiah">Abdelbaset Rabaiah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> e-Government mobile applications provide an extension for effective e-government services in today’s omniconnected world. They constitute part of m-government platforms. This study explores the usefulness, availability, discoverability and maturity of such applications. While this study impacts theory by addressing a relatively lacking area, it impacts practice more. The outcomes of this study suggest valuable recommendations for practitioners-developers of e-government applications. The methodology followed is to examine a large number of e-government smartphone applications. The focus is on applications available at the Google Play Store. Moreover, the study investigates applications published on government portals of a number of countries. A sample of 15 countries is researched. The results show a diversity in the level of discoverability, development, maturity, and usage of smartphone apps dedicated for use of e-government services. It was found that there are major issues in discovering e-government applications on both the Google Play Store and as-well-as on local government portals. The study found that only a fraction of mobile government applications was published on the Play Store. Only 19% of apps were multilingual, and 43% were developed by third parties including private individuals. Further analysis was made, and important recommendations are suggested in this paper for a better utilization of e-government smartphone applications. These recommendations will result in better discoverability, maturity, and usefulness of e-government applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mobile%20applications" title="mobile applications">mobile applications</a>, <a href="https://publications.waset.org/abstracts/search?q=e-government" title=" e-government"> e-government</a>, <a href="https://publications.waset.org/abstracts/search?q=m-government" title=" m-government"> m-government</a>, <a href="https://publications.waset.org/abstracts/search?q=Google%20Play%20Store" title=" Google Play Store"> Google Play Store</a> </p> <a href="https://publications.waset.org/abstracts/98733/state-of-play-of-mobile-government-apps-on-google-play-store" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98733.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">149</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">550</span> SC-LSH: An Efficient Indexing Method for Approximate Similarity Search in High Dimensional Space</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sanaa%20Chafik">Sanaa Chafik</a>, <a href="https://publications.waset.org/abstracts/search?q=Imane%20Daoudi"> Imane Daoudi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mounim%20A.%20El%20Yacoubi"> Mounim A. El Yacoubi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20El%20Ouardi"> Hamid El Ouardi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Locality Sensitive Hashing (LSH) is one of the most promising techniques for solving nearest neighbour search problem in high dimensional space. Euclidean LSH is the most popular variation of LSH that has been successfully applied in many multimedia applications. However, the Euclidean LSH presents limitations that affect structure and query performances. The main limitation of the Euclidean LSH is the large memory consumption. In order to achieve a good accuracy, a large number of hash tables is required. In this paper, we propose a new hashing algorithm to overcome the storage space problem and improve query time, while keeping a good accuracy as similar to that achieved by the original Euclidean LSH. The Experimental results on a real large-scale dataset show that the proposed approach achieves good performances and consumes less memory than the Euclidean LSH. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=approximate%20nearest%20neighbor%20search" title="approximate nearest neighbor search">approximate nearest neighbor search</a>, <a href="https://publications.waset.org/abstracts/search?q=content%20based%20image%20retrieval%20%28CBIR%29" title=" content based image retrieval (CBIR)"> content based image retrieval (CBIR)</a>, <a href="https://publications.waset.org/abstracts/search?q=curse%20of%20dimensionality" title=" curse of dimensionality"> curse of dimensionality</a>, <a href="https://publications.waset.org/abstracts/search?q=locality%20sensitive%20hashing" title=" locality sensitive hashing"> locality sensitive hashing</a>, <a href="https://publications.waset.org/abstracts/search?q=multidimensional%20indexing" title=" multidimensional indexing"> multidimensional indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=scalability" title=" scalability"> scalability</a> </p> <a href="https://publications.waset.org/abstracts/12901/sc-lsh-an-efficient-indexing-method-for-approximate-similarity-search-in-high-dimensional-space" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12901.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">321</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">549</span> Distributed Processing for Content Based Lecture Video Retrieval on Hadoop Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=U.%20S.%20N.%20Raju">U. S. N. Raju</a>, <a href="https://publications.waset.org/abstracts/search?q=Kothuri%20Sai%20Kiran"> Kothuri Sai Kiran</a>, <a href="https://publications.waset.org/abstracts/search?q=Meena%20G.%20Kamal"> Meena G. Kamal</a>, <a href="https://publications.waset.org/abstracts/search?q=Vinay%20Nikhil%20Pabba"> Vinay Nikhil Pabba</a>, <a href="https://publications.waset.org/abstracts/search?q=Suresh%20Kanaparthi"> Suresh Kanaparthi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There is huge amount of lecture video data available for public use, and many more lecture videos are being created and uploaded every day. Searching for videos on required topics from this huge database is a challenging task. Therefore, an efﬁcient method for video retrieval is needed. An approach for automated video indexing and video search in large lecture video archives is presented. As the amount of video lecture data is huge, it is very inefficient to do the processing in a centralized computation framework. Hence, Hadoop Framework for distributed computing for Big Video Data is used. First, step in the process is automatic video segmentation and key-frame detection to offer a visual guideline for the video content navigation. In the next step, we extract textual metadata by applying video Optical Character Recognition (OCR) technology on key-frames. The OCR and detected slide text line types are adopted for keyword extraction, by which both video- and segment-level keywords are extracted for content-based video browsing and search. The performance of the indexing process can be improved for a large database by using distributed computing on Hadoop framework. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20lectures" title="video lectures">video lectures</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20video%20data" title=" big video data"> big video data</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20retrieval" title=" video retrieval"> video retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=hadoop" title=" hadoop"> hadoop</a> </p> <a href="https://publications.waset.org/abstracts/26648/distributed-processing-for-content-based-lecture-video-retrieval-on-hadoop-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26648.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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