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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="hierarchical tag set"> <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> 574</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: hierarchical tag set</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">574</span> Meta-Learning for Hierarchical Classification and Applications in Bioinformatics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fabio%20Fabris">Fabio Fabris</a>, <a href="https://publications.waset.org/abstracts/search?q=Alex%20A.%20Freitas"> Alex A. Freitas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hierarchical classification is a special type of classification task where the class labels are organised into a hierarchy, with more generic class labels being ancestors of more specific ones. Meta-learning for classification-algorithm recommendation consists of recommending to the user a classification algorithm, from a pool of candidate algorithms, for a dataset, based on the past performance of the candidate algorithms in other datasets. Meta-learning is normally used in conventional, non-hierarchical classification. By contrast, this paper proposes a meta-learning approach for more challenging task of hierarchical classification, and evaluates it in a large number of bioinformatics datasets. Hierarchical classification is especially relevant for bioinformatics problems, as protein and gene functions tend to be organised into a hierarchy of class labels. This work proposes meta-learning approach for recommending the best hierarchical classification algorithm to a hierarchical classification dataset. This work’s contributions are: 1) proposing an algorithm for splitting hierarchical datasets into new datasets to increase the number of meta-instances, 2) proposing meta-features for hierarchical classification, and 3) interpreting decision-tree meta-models for hierarchical classification algorithm recommendation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithm%20recommendation" title="algorithm recommendation">algorithm recommendation</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-learning" title=" meta-learning"> meta-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=bioinformatics" title=" bioinformatics"> bioinformatics</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20classification" title=" hierarchical classification"> hierarchical classification</a> </p> <a href="https://publications.waset.org/abstracts/81005/meta-learning-for-hierarchical-classification-and-applications-in-bioinformatics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81005.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">314</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> Hybrid Hierarchical Clustering Approach for Community Detection in Social Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Radhia%20Toujani">Radhia Toujani</a>, <a href="https://publications.waset.org/abstracts/search?q=Jalel%20Akaichi"> Jalel Akaichi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Social Networks generally present a hierarchy of communities. To determine these communities and the relationship between them, detection algorithms should be applied. Most of the existing algorithms, proposed for hierarchical communities identification, are based on either agglomerative clustering or divisive clustering. In this paper, we present a hybrid hierarchical clustering approach for community detection based on both bottom-up and bottom-down clustering. Obviously, our approach provides more relevant community structure than hierarchical method which considers only divisive or agglomerative clustering to identify communities. Moreover, we performed some comparative experiments to enhance the quality of the clustering results and to show the effectiveness of our algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agglomerative%20hierarchical%20clustering" title="agglomerative hierarchical clustering">agglomerative hierarchical clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=community%20structure" title=" community structure"> community structure</a>, <a href="https://publications.waset.org/abstracts/search?q=divisive%20hierarchical%20clustering" title=" divisive hierarchical clustering"> divisive hierarchical clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20hierarchical%20clustering" title=" hybrid hierarchical clustering"> hybrid hierarchical clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=opinion%20mining" title=" opinion mining"> opinion mining</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20network" title=" social network"> social network</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20network%20analysis" title=" social network analysis"> social network analysis</a> </p> <a href="https://publications.waset.org/abstracts/63702/hybrid-hierarchical-clustering-approach-for-community-detection-in-social-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63702.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">572</span> Hierarchical Clustering Algorithms in Data Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Z.%20Abdullah">Z. Abdullah</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20R.%20Hamdan"> A. R. Hamdan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clustering is a process of grouping objects and data into groups of clusters to ensure that data objects from the same cluster are identical to each other. Clustering algorithms in one of the areas in data mining and it can be classified into partition, hierarchical, density based, and grid-based. Therefore, in this paper, we do a survey and review for four major hierarchical clustering algorithms called CURE, ROCK, CHAMELEON, and BIRCH. The obtained state of the art of these algorithms will help in eliminating the current problems, as well as deriving more robust and scalable algorithms for clustering. <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=unsupervised%20learning" title=" unsupervised learning"> unsupervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithms" title=" algorithms"> algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical" title=" hierarchical"> hierarchical</a> </p> <a href="https://publications.waset.org/abstracts/31217/hierarchical-clustering-algorithms-in-data-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31217.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">885</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> Knowledge Discovery from Production Databases for Hierarchical Process Control</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pavol%20Tanuska">Pavol Tanuska</a>, <a href="https://publications.waset.org/abstracts/search?q=Pavel%20Vazan"> Pavel Vazan</a>, <a href="https://publications.waset.org/abstracts/search?q=Michal%20Kebisek"> Michal Kebisek</a>, <a href="https://publications.waset.org/abstracts/search?q=Dominika%20Jurovata"> Dominika Jurovata</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper gives the results of the project that was oriented on the usage of knowledge discoveries from production systems for needs of the hierarchical process control. One of the main project goals was the proposal of knowledge discovery model for process control. Specifics data mining methods and techniques was used for defined problems of the process control. The gained knowledge was used on the real production system, thus, the proposed solution has been verified. The paper documents how it is possible to apply new discovery knowledge to be used in the real hierarchical process control. There are specified the opportunities for application of the proposed knowledge discovery model for hierarchical process control. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20process%20control" title="hierarchical process control">hierarchical process control</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20from%20databases" title=" knowledge discovery from databases"> knowledge discovery from databases</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20control" title=" process control"> process control</a> </p> <a href="https://publications.waset.org/abstracts/2816/knowledge-discovery-from-production-databases-for-hierarchical-process-control" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2816.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">570</span> Why Do We Need Hierachical Linear Models?</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mustafa%20Ayd%C4%B1n">Mustafa Aydın</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Murat%20Sunbul"> Ali Murat Sunbul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hierarchical or nested data structures usually are seen in many research areas. Especially, in the field of education, if we examine most of the studies, we can see the nested structures. Students in classes, classes in schools, schools in cities and cities in regions are similar nested structures. In a hierarchical structure, students being in the same class, sharing the same physical conditions and similar experiences and learning from the same teachers, they demonstrate similar behaviors between them rather than the students in other classes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20linear%20modeling" title="hierarchical linear modeling">hierarchical linear modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=nested%20data" title=" nested data"> nested data</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20structure" title="hierarchical structure">hierarchical structure</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20structure" title=" data structure "> data structure </a> </p> <a href="https://publications.waset.org/abstracts/2470/why-do-we-need-hierachical-linear-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2470.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">652</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> Hydrothermally Fabricated 3-D Nanostructure Metal Oxide Sensors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Alenezi">Mohammad Alenezi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hierarchical nanostructures with higher dimensionality, consisting of nanostructure building blocks such as nanowires, nanotubes, or nanosheets are very attractive. They hold great properties like the high surface-to-volume ratio and well-ordered porous structures, which can be very challenging to attain for other mono-morphological nanostructures. Well-ordered hierarchical nanostructures with high surface-to-volume ratios facilitate gas diffusion into their surfaces as well as scattering of light. Therefore, hierarchical nanostructures are expected to perform highly as gas sensors. A multistage controlled hydrothermal synthesis method to fabricate high-performance single ZnO brushlike hierarchical nanostructure gas sensor from initial nanowires is reported. The performance of the sensor based on brush-like hierarchical nanostructure is analyzed and compared to that of a nanowire gas sensor. The hierarchical gas sensor demonstrated high sensitivity toward low concentration of acetone at high speed of response. The enhancement in the hierarchical sensor performance is attributed to the increased surface to volume ratio, reduction in dimensionality of the nanowire building blocks, formation of junctions between the initial nanowire and the secondary nanowires, and enhanced gas diffusion into the surfaces of the hierarchical nanostructures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=metal%20oxide" title="metal oxide">metal oxide</a>, <a href="https://publications.waset.org/abstracts/search?q=nanostructure" title=" nanostructure"> nanostructure</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrothermal" title=" hydrothermal"> hydrothermal</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor" title=" sensor"> sensor</a> </p> <a href="https://publications.waset.org/abstracts/50686/hydrothermally-fabricated-3-d-nanostructure-metal-oxide-sensors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50686.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">272</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> A Model Based Metaheuristic for Hybrid Hierarchical Community Structure in Social Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Radhia%20Toujani">Radhia Toujani</a>, <a href="https://publications.waset.org/abstracts/search?q=Jalel%20Akaichi"> Jalel Akaichi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, the study of community detection in social networks has received great attention. The hierarchical structure of the network leads to the emergence of the convergence to a locally optimal community structure. In this paper, we aim to avoid this local optimum in the introduced hybrid hierarchical method. To achieve this purpose, we present an objective function where we incorporate the value of structural and semantic similarity based modularity and a metaheuristic namely bees colonies algorithm to optimize our objective function on both hierarchical level divisive and agglomerative. In order to assess the efficiency and the accuracy of the introduced hybrid bee colony model, we perform an extensive experimental evaluation on both synthetic and real networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=social%20network" title="social network">social network</a>, <a href="https://publications.waset.org/abstracts/search?q=community%20detection" title=" community detection"> community detection</a>, <a href="https://publications.waset.org/abstracts/search?q=agglomerative%20hierarchical%20clustering" title=" agglomerative hierarchical clustering"> agglomerative hierarchical clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=divisive%20hierarchical%20clustering" title=" divisive hierarchical clustering"> divisive hierarchical clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity" title=" similarity"> similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=modularity" title=" modularity"> modularity</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristic" title=" metaheuristic"> metaheuristic</a>, <a href="https://publications.waset.org/abstracts/search?q=bee%20colony" title=" bee colony"> bee colony</a> </p> <a href="https://publications.waset.org/abstracts/64745/a-model-based-metaheuristic-for-hybrid-hierarchical-community-structure-in-social-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64745.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">379</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> An E-Assessment Website to Implement Hierarchical Aggregate Assessment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Lesage">M. Lesage</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Ra%C3%AEche"> G. Raîche</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Riopel"> M. Riopel</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Fortin"> F. Fortin</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Sebkhi"> D. Sebkhi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes a Web server implementation of the hierarchical aggregate assessment process in the field of education. This process describes itself as a field of teamwork assessment where teams can have multiple levels of hierarchy and supervision. This process is applied everywhere and is part of the management, education, assessment and computer science fields. The E-Assessment website named “Cluster” records in its database the students, the course material, the teams and the hierarchical relationships between the students. For the present research, the hierarchical relationships are team member, team leader and group administrator appointments. The group administrators have the responsibility to supervise team leaders. The experimentation of the application has been performed by high school students in geology courses and Canadian army cadets for navigation patrols in teams. This research extends the work of Nance that uses a hierarchical aggregation process similar as the one implemented in the “Cluster” application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=e-learning" title="e-learning">e-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=e-assessment" title=" e-assessment"> e-assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=teamwork%20assessment" title=" teamwork assessment"> teamwork assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20aggregate%20assessment" title=" hierarchical aggregate assessment"> hierarchical aggregate assessment</a> </p> <a href="https://publications.waset.org/abstracts/2666/an-e-assessment-website-to-implement-hierarchical-aggregate-assessment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2666.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">369</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> Agglomerative Hierarchical Clustering Using the Tθ 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 Tθ 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=" Tθ measures"> Tθ 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">565</span> Digital Geography and Geographic Information System in Schools: Towards a Hierarchical Geospatial Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mary%20Fargher">Mary Fargher</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper examines the opportunities of using a more hierarchical approach to geospatial enquiry in using GIS in school geography. A case is made that it is not just the lack of teacher technological knowledge that is stopping some teachers from using GIS in the classroom but that there is a gap in their understanding of how to link GIS use more specifically to the pedagogy of teaching geography with GIS. Using a hierarchical approach to geospatial enquiry as a theoretical framework, the analysis shows clearly how concepts of spatial distribution, interaction, relation, comparison, and temporal relationships can be used by teachers more explicitly to capitalise on the analytical power of GIS and to construct what can be interpreted as powerful geographical knowledge. An exemplar illustrating this approach on the topic of geo-hazards is then presented for critical analysis and discussion. Recommendations are then made for a model of progression for geography teacher education with GIS through hierarchical geospatial enquiry that takes into account beginner, intermediate, and more advanced users. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20geography" title="digital geography">digital geography</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=education" title=" education"> education</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20geospatial%20enquiry" title=" hierarchical geospatial enquiry"> hierarchical geospatial enquiry</a>, <a href="https://publications.waset.org/abstracts/search?q=powerful%20geographical%20knowledge" title=" powerful geographical knowledge"> powerful geographical knowledge</a> </p> <a href="https://publications.waset.org/abstracts/125215/digital-geography-and-geographic-information-system-in-schools-towards-a-hierarchical-geospatial-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/125215.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">152</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> Semi-Supervised Hierarchical Clustering Given a Reference Tree of Labeled Documents</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ying%20Zhao">Ying Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Xingyan%20Bin"> Xingyan Bin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Semi-supervised clustering algorithms have been shown effective to improve clustering process with even limited supervision. However, semi-supervised hierarchical clustering remains challenging due to the complexities of expressing constraints for agglomerative clustering algorithms. This paper proposes novel semi-supervised agglomerative clustering algorithms to build a hierarchy based on a known reference tree. We prove that by enforcing distance constraints defined by a reference tree during the process of hierarchical clustering, the resultant tree is guaranteed to be consistent with the reference tree. We also propose a framework that allows the hierarchical tree generation be aware of levels of levels of the agglomerative tree under creation, so that metric weights can be learned and adopted at each level in a recursive fashion. The experimental evaluation shows that the additional cost of our contraint-based semi-supervised hierarchical clustering algorithm (HAC) is negligible, and our combined semi-supervised HAC algorithm outperforms the state-of-the-art algorithms on real-world datasets. The experiments also show that our proposed methods can improve clustering performance even with a small number of unevenly distributed labeled data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semi-supervised%20clustering" title="semi-supervised clustering">semi-supervised clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%0D%0Aagglomerative%20clustering" title=" hierarchical agglomerative clustering"> hierarchical agglomerative clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=reference%20trees" title=" reference trees"> reference trees</a>, <a href="https://publications.waset.org/abstracts/search?q=distance%20constraints" title=" distance constraints "> distance constraints </a> </p> <a href="https://publications.waset.org/abstracts/19478/semi-supervised-hierarchical-clustering-given-a-reference-tree-of-labeled-documents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19478.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">547</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> Performance Analysis of Hierarchical Agglomerative Clustering in a Wireless Sensor Network Using Quantitative Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tapan%20Jain">Tapan Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Davender%20Singh%20Saini"> Davender Singh Saini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clustering is a useful mechanism in wireless sensor networks which helps to cope with scalability and data transmission problems. The basic aim of our research work is to provide efficient clustering using Hierarchical agglomerative clustering (HAC). If the distance between the sensing nodes is calculated using their location then it’s quantitative HAC. This paper compares the various agglomerative clustering techniques applied in a wireless sensor network using the quantitative data. The simulations are done in MATLAB and the comparisons are made between the different protocols using dendrograms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=routing" title="routing">routing</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20clustering" title=" hierarchical clustering"> hierarchical clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=agglomerative" title=" agglomerative"> agglomerative</a>, <a href="https://publications.waset.org/abstracts/search?q=quantitative" title=" quantitative"> quantitative</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20network" title=" wireless sensor network"> wireless sensor network</a> </p> <a href="https://publications.waset.org/abstracts/3593/performance-analysis-of-hierarchical-agglomerative-clustering-in-a-wireless-sensor-network-using-quantitative-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3593.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">615</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> Recurrent Neural Networks with Deep Hierarchical Mixed Structures for Chinese Document Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhaoxin%20Luo">Zhaoxin Luo</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Zhu"> Michael Zhu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In natural languages, there are always complex semantic hierarchies. Obtaining the feature representation based on these complex semantic hierarchies becomes the key to the success of the model. Several RNN models have recently been proposed to use latent indicators to obtain the hierarchical structure of documents. However, the model that only uses a single-layer latent indicator cannot achieve the true hierarchical structure of the language, especially a complex language like Chinese. In this paper, we propose a deep layered model that stacks arbitrarily many RNN layers equipped with latent indicators. After using EM and training it hierarchically, our model solves the computational problem of stacking RNN layers and makes it possible to stack arbitrarily many RNN layers. Our deep hierarchical model not only achieves comparable results to large pre-trained models on the Chinese short text classification problem but also achieves state of art results on the Chinese long text classification problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nature%20language%20processing" title="nature language processing">nature language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=recurrent%20neural%20network" title=" recurrent neural network"> recurrent neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20structure" title=" hierarchical structure"> hierarchical structure</a>, <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=Chinese" title=" Chinese"> Chinese</a> </p> <a href="https://publications.waset.org/abstracts/171867/recurrent-neural-networks-with-deep-hierarchical-mixed-structures-for-chinese-document-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171867.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">68</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> Facile Hydrothermal Synthesis of Hierarchical NiO/ZnCo₂O₄ Nanocomposite for High-Energy Supercapacitor Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fayssal%20Ynineb">Fayssal Ynineb</a>, <a href="https://publications.waset.org/abstracts/search?q=Toufik%20Hadjersi"> Toufik Hadjersi</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatsah%20Moulai"> Fatsah Moulai</a>, <a href="https://publications.waset.org/abstracts/search?q=Wafa%20Achour"> Wafa Achour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Currently, tremendous attention has been paid to the rational design and synthesis of core/shell heterostructures for high-performance supercapacitors. In this study, the hierarchical NiO/ZnCo₂O₄ Core-Shell Nanorods Arrays were successfully deposited onto ITO substrate via a two-step hydrothermal and electrodeposition methods. The effect of the thin carbon layer between NiO and ZnCo₂O₄ in this multi-scale hierarchical structure was investigated. The selection of this structure was based on: (i) a high specific area of pseudo-capacitive NiO to maximize specific capacitance; (ii) an effective NiO-electrolyte interface to facilitate fast charging/discharging; and (iii) conducting carbon layer between ZnCo₂O₄ and NiO enhance the electric conductivity which reduces energy loss, and the corrosion protection of ZnCo₂O₄ in alkaline electrolyte. The obtained results indicate that hierarchical NiO/ZnCo₂O₄ present a high specific capacitance of 63 mF.cm⁻² at a current density of 0.05 mA.cm⁻² higher than that of pristine NiO and ZnCo₂O₄ of 6 and 3 mF.cm⁻², respectively. The carbon layer improves the electrical conductivity among NiO and ZnCo₂O₄ in the hierarchical NiO/C/ZnCo₂O₄ electrode. As well, the specific capacitance drastically increased to reach 125 mF.cm⁻². Moreover, this multi-scale hierarchical structure exhibits superior cycling stability with ~ 95.7 % capacitance retention after 65k cycles. These results indicate that the NiO/C/ZnCo₂O₄ nanocomposite material is an outstanding electrode material for supercapacitors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=NiO%2FC%2FZnCo%E2%82%82O%E2%82%84" title="NiO/C/ZnCo₂O₄">NiO/C/ZnCo₂O₄</a>, <a href="https://publications.waset.org/abstracts/search?q=specific%20capacitance" title=" specific capacitance"> specific capacitance</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrothermal" title=" hydrothermal"> hydrothermal</a>, <a href="https://publications.waset.org/abstracts/search?q=supercapacitors" title=" supercapacitors"> supercapacitors</a> </p> <a href="https://publications.waset.org/abstracts/158685/facile-hydrothermal-synthesis-of-hierarchical-nioznco2o4-nanocomposite-for-high-energy-supercapacitor-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158685.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">99</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> Spatial Econometric Approaches for Count Data: An Overview and New Directions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Paula%20Sim%C3%B5es">Paula Simões</a>, <a href="https://publications.waset.org/abstracts/search?q=Isabel%20Nat%C3%A1rio"> Isabel Natário</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper reviews a number of theoretical aspects for implementing an explicit spatial perspective in econometrics for modelling non-continuous data, in general, and count data, in particular. It provides an overview of the several spatial econometric approaches that are available to model data that are collected with reference to location in space, from the classical spatial econometrics approaches to the recent developments on spatial econometrics to model count data, in a Bayesian hierarchical setting. Considerable attention is paid to the inferential framework, necessary for structural consistent spatial econometric count models, incorporating spatial lag autocorrelation, to the corresponding estimation and testing procedures for different assumptions, to the constrains and implications embedded in the various specifications in the literature. This review combines insights from the classical spatial econometrics literature as well as from hierarchical modeling and analysis of spatial data, in order to look for new possible directions on the processing of count data, in a spatial hierarchical Bayesian econometric context. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spatial%20data%20analysis" title="spatial data analysis">spatial data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20econometrics" title=" spatial econometrics"> spatial econometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20hierarchical%20models" title=" Bayesian hierarchical models"> Bayesian hierarchical models</a>, <a href="https://publications.waset.org/abstracts/search?q=count%20data" title=" count data"> count data</a> </p> <a href="https://publications.waset.org/abstracts/35788/spatial-econometric-approaches-for-count-data-an-overview-and-new-directions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35788.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">593</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 Use of Layered Neural Networks for Classifying Hierarchical Scientific Fields of Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Colin%20Smith">Colin Smith</a>, <a href="https://publications.waset.org/abstracts/search?q=Linsey%20S%20Passarella"> Linsey S Passarella</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the proliferation and decentralized nature of academic publication, no widely accepted scheme exists for organizing papers by their scientific field of study (FoS) to the author’s best knowledge. While many academic journals require author provided keywords for papers, these keywords range wildly in scope and are not consistent across papers, journals, or field domains, necessitating alternative approaches to paper classification. Past attempts to perform field-of-study (FoS) classification on scientific texts have largely used a-hierarchical FoS schemas or ignored the schema’s inherently hierarchical structure, e.g. by compressing the structure into a single layer for multi-label classification. In this paper, we introduce an application of a Layered Neural Network (LNN) to the problem of performing supervised hierarchical classification of scientific fields of study (FoS) on research papers. In this approach, paper embeddings from a pretrained language model are fed into a top-down LNN. Beginning with a single neural network (NN) for the highest layer of the class hierarchy, each node uses a separate local NN to classify the subsequent subfield child node(s) for an input embedding of concatenated paper titles and abstracts. We compare our LNN-FOS method to other recent machine learning methods using the Microsoft Academic Graph (MAG) FoS hierarchy and find that the LNN-FOS offers increased classification accuracy at each FoS hierarchical level. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20classification" title="hierarchical classification">hierarchical classification</a>, <a href="https://publications.waset.org/abstracts/search?q=layer%20neural%20network" title=" layer neural network"> layer neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=scientific%20field%20of%20study" title=" scientific field of study"> scientific field of study</a>, <a href="https://publications.waset.org/abstracts/search?q=scientific%20taxonomy" title=" scientific taxonomy"> scientific taxonomy</a> </p> <a href="https://publications.waset.org/abstracts/151193/the-use-of-layered-neural-networks-for-classifying-hierarchical-scientific-fields-of-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151193.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">133</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> Effects of Hierarchy on Poisson’s Ratio and Phononic Bandgaps of Two-Dimensional Honeycomb Structures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Davood%20Mousanezhad">Davood Mousanezhad</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashkan%20Vaziri"> Ashkan Vaziri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As a traditional cellular structure, hexagonal honeycombs are known for their high strength-to-weight ratio. Here, we introduce a class of fractal-appearing hierarchical metamaterials by replacing the vertices of the original non-hierarchical hexagonal grid with smaller hexagons and iterating this process to achieve higher levels of hierarchy. It has been recently shown that the isotropic in-plane Young's modulus of this hierarchical structure at small deformations becomes 25 times greater than its regular counterpart with the same mass. At large deformations, we find that hierarchy-dependent elastic buckling introduced at relatively early stages of deformation decreases the value of Poisson's ratio as the structure is compressed uniaxially leading to auxeticity (i.e., negative Poisson's ratio) in subsequent stages of deformation. We also show that the topological hierarchical architecture and instability-induced pattern transformations of the structure under compression can be effectively used to tune the propagation of elastic waves within the structure. We find that the hierarchy tends to shift the existing phononic bandgaps (defined as frequency ranges of strong wave attenuation) to lower frequencies while opening up new bandgaps. Deformation is also demonstrated as another mechanism for opening more bandgaps in hierarchical structures. The results provide new insights into the role of structural organization and hierarchy in regulating mechanical properties of materials at both the static and dynamic regimes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cellular%20structures" title="cellular structures">cellular structures</a>, <a href="https://publications.waset.org/abstracts/search?q=honeycombs" title=" honeycombs"> honeycombs</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20structures" title=" hierarchical structures"> hierarchical structures</a>, <a href="https://publications.waset.org/abstracts/search?q=metamaterials" title=" metamaterials"> metamaterials</a>, <a href="https://publications.waset.org/abstracts/search?q=multifunctional%20structures" title=" multifunctional structures"> multifunctional structures</a>, <a href="https://publications.waset.org/abstracts/search?q=phononic%20crystals" title=" phononic crystals"> phononic crystals</a>, <a href="https://publications.waset.org/abstracts/search?q=auxetic%20structures" title=" auxetic structures"> auxetic structures</a> </p> <a href="https://publications.waset.org/abstracts/51508/effects-of-hierarchy-on-poissons-ratio-and-phononic-bandgaps-of-two-dimensional-honeycomb-structures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51508.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">349</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> A Probabilistic View of the Spatial Pooler in Hierarchical Temporal Memory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mackenzie%20Leake">Mackenzie Leake</a>, <a href="https://publications.waset.org/abstracts/search?q=Liyu%20Xia"> Liyu Xia</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamil%20Rocki"> Kamil Rocki</a>, <a href="https://publications.waset.org/abstracts/search?q=Wayne%20Imaino"> Wayne Imaino</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the Hierarchical Temporal Memory (HTM) paradigm the effect of overlap between inputs on the activation of columns in the spatial pooler is studied. Numerical results suggest that similar inputs are represented by similar sets of columns and dissimilar inputs are represented by dissimilar sets of columns. It is shown that the spatial pooler produces these results under certain conditions for the connectivity and proximal thresholds. Following the discussion of the initialization of parameters for the thresholds, corresponding qualitative arguments about the learning dynamics of the spatial pooler are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20temporal%20memory" title="hierarchical temporal memory">hierarchical temporal memory</a>, <a href="https://publications.waset.org/abstracts/search?q=HTM" title=" HTM"> HTM</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20algorithms" title=" learning algorithms"> learning algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20pooler" title=" spatial pooler"> spatial pooler</a> </p> <a href="https://publications.waset.org/abstracts/29210/a-probabilistic-view-of-the-spatial-pooler-in-hierarchical-temporal-memory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29210.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">345</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> Hierarchical Tree Long Short-Term Memory for Sentence Representations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiuying%20Wang">Xiuying Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Changliang%20Li"> Changliang Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Bo%20Xu"> Bo Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A fixed-length feature vector is required for many machine learning algorithms in NLP field. Word embeddings have been very successful at learning lexical information. However, they cannot capture the compositional meaning of sentences, which prevents them from a deeper understanding of language. In this paper, we introduce a novel hierarchical tree long short-term memory (HTLSTM) model that learns vector representations for sentences of arbitrary syntactic type and length. We propose to split one sentence into three hierarchies: short phrase, long phrase and full sentence level. The HTLSTM model gives our algorithm the potential to fully consider the hierarchical information and long-term dependencies of language. We design the experiments on both English and Chinese corpus to evaluate our model on sentiment analysis task. And the results show that our model outperforms several existing state of the art approaches significantly. <p class="card-text"><strong>Keywords:</strong> <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=hierarchical%20tree%20long%20short-term%20memory" title=" hierarchical tree long short-term memory"> hierarchical tree long short-term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=sentence%20representation" title=" sentence representation"> sentence representation</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a> </p> <a href="https://publications.waset.org/abstracts/83787/hierarchical-tree-long-short-term-memory-for-sentence-representations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83787.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">349</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> Cooperative CDD Scheme Based On Hierarchical Modulation in OFDM System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seung-Jun%20Yu">Seung-Jun Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yeong-Seop%20Ahn"> Yeong-Seop Ahn</a>, <a href="https://publications.waset.org/abstracts/search?q=Young-Min%20Ko"> Young-Min Ko</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyoung-Kyu%20Song">Hyoung-Kyu Song</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to achieve high data rate and increase the spectral efficiency, multiple input multiple output (MIMO) system has been proposed. However, multiple antennas are limited by size and cost. Therefore, recently developed cooperative diversity scheme, which profits the transmit diversity only with the existing hardware by constituting a virtual antenna array, can be a solution. However, most of the introduced cooperative techniques have a common fault of decreased transmission rate because the destination should receive the decodable compositions of symbols from the source and the relay. In this paper, we propose a cooperative cyclic delay diversity (CDD) scheme that uses hierarchical modulation. This scheme is free from the rate loss and allows seamless cooperative communication. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MIMO" title="MIMO">MIMO</a>, <a href="https://publications.waset.org/abstracts/search?q=cooperative%20communication" title=" cooperative communication"> cooperative communication</a>, <a href="https://publications.waset.org/abstracts/search?q=CDD" title=" CDD"> CDD</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20modulation" title=" hierarchical modulation"> hierarchical modulation</a> </p> <a href="https://publications.waset.org/abstracts/32286/cooperative-cdd-scheme-based-on-hierarchical-modulation-in-ofdm-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32286.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">549</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> The Problems of Current Earth Coordinate System for Earthquake Forecasting Using Single Layer Hierarchical Graph Neuron</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Benny%20Benyamin%20Nasution">Benny Benyamin Nasution</a>, <a href="https://publications.waset.org/abstracts/search?q=Rahmat%20Widia%20Sembiring"> Rahmat Widia Sembiring</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdul%20Rahman%20Dalimunthe"> Abdul Rahman Dalimunthe</a>, <a href="https://publications.waset.org/abstracts/search?q=Nursiah%20Mustari"> Nursiah Mustari</a>, <a href="https://publications.waset.org/abstracts/search?q=Nisfan%20Bahri"> Nisfan Bahri</a>, <a href="https://publications.waset.org/abstracts/search?q=Berta%20br%20Ginting"> Berta br Ginting</a>, <a href="https://publications.waset.org/abstracts/search?q=Riadil%20Akhir%20Lubis"> Riadil Akhir Lubis</a>, <a href="https://publications.waset.org/abstracts/search?q=Rita%20Tavip%20Megawati"> Rita Tavip Megawati</a>, <a href="https://publications.waset.org/abstracts/search?q=Indri%20Dithisari"> Indri Dithisari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The earth coordinate system is an important part of an attempt for earthquake forecasting, such as the one using Single Layer Hierarchical Graph Neuron (SLHGN). However, there are a number of problems that need to be worked out before the coordinate system can be utilized for the forecaster. One example of those is that SLHGN requires that the focused area of an earthquake must be constructed in a grid-like form. In fact, within the current earth coordinate system, the same longitude-difference would produce different distances. This can be observed at the distance on the Equator compared to distance at both poles. To deal with such a problem, a coordinate system has been developed, so that it can be used to support the ongoing earthquake forecasting using SLHGN. Two important issues have been developed in this system: 1) each location is not represented through two-value (longitude and latitude), but only a single value, 2) the conversion of the earth coordinate system to the x-y cartesian system requires no angular formulas, which is therefore fast. The accuracy and the performance have not been measured yet, since earthquake data is difficult to obtain. However, the characteristics of the SLHGN results show a very promising answer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20graph%20neuron" title="hierarchical graph neuron">hierarchical graph neuron</a>, <a href="https://publications.waset.org/abstracts/search?q=multidimensional%20hierarchical%20graph%20neuron" title=" multidimensional hierarchical graph neuron"> multidimensional hierarchical graph neuron</a>, <a href="https://publications.waset.org/abstracts/search?q=single%20layer%20hierarchical%20graph%20neuron" title=" single layer hierarchical graph neuron"> single layer hierarchical graph neuron</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20disaster%20forecasting" title=" natural disaster forecasting"> natural disaster forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=earthquake%20forecasting" title=" earthquake forecasting"> earthquake forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=earth%20coordinate%20system" title=" earth coordinate system"> earth coordinate system</a> </p> <a href="https://publications.waset.org/abstracts/118471/the-problems-of-current-earth-coordinate-system-for-earthquake-forecasting-using-single-layer-hierarchical-graph-neuron" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118471.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">216</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> An Improved Approach to Solve Two-Level Hierarchical Time Minimization Transportation Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kalpana%20Dahiya">Kalpana Dahiya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses a two-level hierarchical time minimization transportation problem, which is an important class of transportation problems arising in industries. This problem has been studied by various researchers, and a number of polynomial time iterative algorithms are available to find its solution. All the existing algorithms, though efficient, have some shortcomings. The current study proposes an alternate solution algorithm for the problem that is more efficient in terms of computational time than the existing algorithms. The results justifying the underlying theory of the proposed algorithm are given. Further, a detailed comparison of the computational behaviour of all the algorithms for randomly generated instances of this problem of different sizes validates the efficiency of the proposed algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=global%20optimization" title="global optimization">global optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20optimization" title=" hierarchical optimization"> hierarchical optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=transportation%20problem" title=" transportation problem"> transportation problem</a>, <a href="https://publications.waset.org/abstracts/search?q=concave%20minimization" title=" concave minimization"> concave minimization</a> </p> <a href="https://publications.waset.org/abstracts/122713/an-improved-approach-to-solve-two-level-hierarchical-time-minimization-transportation-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122713.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">162</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> Identification of Spam Keywords Using Hierarchical Category in C2C E-Commerce</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shao%20Bo%20Cheng">Shao Bo Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Yong-Jin%20Han"> Yong-Jin Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Se%20Young%20Park"> Se Young Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Seong-Bae%20Park"> Seong-Bae Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Consumer-to-Consumer (C2C) E-commerce has been growing at a very high speed in recent years. Since identical or nearly-same kinds of products compete one another by relying on keyword search in C2C E-commerce, some sellers describe their products with spam keywords that are popular but are not related to their products. Though such products get more chances to be retrieved and selected by consumers than those without spam keywords, the spam keywords mislead the consumers and waste their time. This problem has been reported in many commercial services like e-bay and taobao, but there have been little research to solve this problem. As a solution to this problem, this paper proposes a method to classify whether keywords of a product are spam or not. The proposed method assumes that a keyword for a given product is more reliable if the keyword is observed commonly in specifications of products which are the same or the same kind as the given product. This is because that a hierarchical category of a product in general determined precisely by a seller of the product and so is the specification of the product. Since higher layers of the hierarchical category represent more general kinds of products, a reliable degree is differently determined according to the layers. Hence, reliable degrees from different layers of a hierarchical category become features for keywords and they are used together with features only from specifications for classification of the keywords. Support Vector Machines are adopted as a basic classifier using the features, since it is powerful, and widely used in many classification tasks. In the experiments, the proposed method is evaluated with a golden standard dataset from Yi-han-wang, a Chinese C2C e-commerce, and is compared with a baseline method that does not consider the hierarchical category. The experimental results show that the proposed method outperforms the baseline in F1-measure, which proves that spam keywords are effectively identified by a hierarchical category in C2C e-commerce. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spam%20keyword" title="spam keyword">spam keyword</a>, <a href="https://publications.waset.org/abstracts/search?q=e-commerce" title=" e-commerce"> e-commerce</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword%20features" title=" keyword features"> keyword features</a>, <a href="https://publications.waset.org/abstracts/search?q=spam%20%EF%AC%81ltering" title=" spam ﬁltering"> spam ﬁltering</a> </p> <a href="https://publications.waset.org/abstracts/15501/identification-of-spam-keywords-using-hierarchical-category-in-c2c-e-commerce" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15501.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">294</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> Comparative Analysis of Effecting Factors on Fertility by Birth Order: A Hierarchical Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Hesari">Ali Hesari</a>, <a href="https://publications.waset.org/abstracts/search?q=Arezoo%20Esmaeeli"> Arezoo Esmaeeli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Regarding to dramatic changes of fertility and higher order births during recent decades in Iran, access to knowledge about affecting factors on different birth orders has crucial importance. In this study, According to hierarchical structure of many of social sciences data and the effect of variables of different levels of social phenomena that determine different birth orders in 365 days ending to 1390 census have been explored by multilevel approach. In this paper, 2% individual row data for 1390 census is analyzed by HLM software. Three different hierarchical linear regression models are estimated for data analysis of the first and second, third, fourth and more birth order. Research results displays different outcomes for three models. Individual level variables entered in equation are; region of residence (rural/urban), age, educational level and labor participation status and province level variable is GDP per capita. Results show that individual level variables have different effects in these three models and in second level we have different random and fixed effects in these models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fertility" title="fertility">fertility</a>, <a href="https://publications.waset.org/abstracts/search?q=birth%20order" title=" birth order"> birth order</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20approach" title=" hierarchical approach"> hierarchical approach</a>, <a href="https://publications.waset.org/abstracts/search?q=fixe%20effects" title=" fixe effects"> fixe effects</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20effects" title=" random effects"> random effects</a> </p> <a href="https://publications.waset.org/abstracts/22660/comparative-analysis-of-effecting-factors-on-fertility-by-birth-order-a-hierarchical-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22660.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">339</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> Direct Electrophoretic Deposition of Hierarchical Structured Electrode Supercapacitor Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jhen-Ting%20Huang">Jhen-Ting Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chia-Chia%20Chang"> Chia-Chia Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hu-Cheng%20Weng"> Hu-Cheng Weng</a>, <a href="https://publications.waset.org/abstracts/search?q=An-Ya%20Lo"> An-Ya Lo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, Co3O4-CNT-Graphene composite electrode was deposited by electrophoretic deposition (EPD) method, where micro polystyrene spheres (PSs) were added for co-deposition. Applied with heat treatment, a hierarchical porosity is left in the electrode which is beneficial for supercapacitor application. In terms of charge and discharge performance, we discussed the optimal CNT/Graphene ratio, macroporous ratio, and the effect of Co3O4 addition on electrode capacitance. For materials characterization, scanning electron microscope (SEM), X-ray diffraction, and BET were applied, while cyclic voltammetry (CV) and chronopotentiometry (CP) measurements, and Ragone plot were applied as in-situ analyses. Based on this, the effects of PS amount on the structure, porosity and their effect on capacitance of the electrodes were investigated. Finally, the full device performance was examined with charge-discharge and electron impedance spectrum (EIS) methods. The results show that the EPD coating with hierarchical porosity was successfully demonstrated in this study. As a result, the capacitance was greatly enhanced by 2.6 times with the hierarchical structure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=supercapacitor" title="supercapacitor">supercapacitor</a>, <a href="https://publications.waset.org/abstracts/search?q=nanocarbon%20tub" title=" nanocarbon tub"> nanocarbon tub</a>, <a href="https://publications.waset.org/abstracts/search?q=graphene" title=" graphene"> graphene</a>, <a href="https://publications.waset.org/abstracts/search?q=metal%20oxide" title=" metal oxide"> metal oxide</a> </p> <a href="https://publications.waset.org/abstracts/107788/direct-electrophoretic-deposition-of-hierarchical-structured-electrode-supercapacitor-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107788.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">139</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> Hierarchical Zeolites as Potential Carriers of Curcumin</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ewelina%20Musielak">Ewelina Musielak</a>, <a href="https://publications.waset.org/abstracts/search?q=Agnieszka%20Feliczak-Guzik"> Agnieszka Feliczak-Guzik</a>, <a href="https://publications.waset.org/abstracts/search?q=Izabela%20Nowak"> Izabela Nowak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Based on the latest data, it is expected that the substances of therapeutic interest used will be as natural as possible. Therefore, active substances with the highest possible efficacy and low toxicity are sought. Among natural substances with therapeutic effects, those of plant origin stand out. Curcumin isolated from the Curcuma longa plant has proven to be particularly important from a medical point of view. Due to its ability to regulate many important transcription factors, cytokines, and protein kinases, curcumin has found use as an anti-inflammatory, antioxidant, antiproliferative, antiangiogenic, and anticancer agent. The unfavorable properties of curcumin, such as low solubility, poor bioavailability, and rapid degradation under neutral or alkaline pH conditions, limit its clinical application. These problems can be solved by combining curcumin with suitable carriers such as hierarchical zeolites. This is a new class of materials that exhibit several advantages. Hierarchical zeolites used as drug carriers enable delayed release of the active ingredient and promote drug transport to the desired tissues and organs. In addition, hierarchical zeolites play an important role in regulating micronutrient levels in the body and have been used successfully in cancer diagnosis and therapy. To apply curcumin to hierarchical zeolites synthesized from commercial FAU zeolite, solutions containing curcumin, carrier and acetone were prepared. The prepared mixtures were then stirred on a magnetic stirrer for 24 h at room temperature. The curcumin-filled hierarchical zeolites were drained into a glass funnel, where they were washed three times with acetone and distilled water, after which the obtained material was air-dried until completely dry. In addition, the effect of piperine addition to zeolite carrier containing a sufficient amount of curcumin was studied. The resulting products were weighed and the percentage of pure curcumin in the hierarchical zeolite was calculated. All the synthesized materials were characterized by several techniques: elemental analysis, transmission electron microscopy (TEM), Fourier transform infrared spectroscopy, Fourier transform infrared (FT-IR), N2 adsorption, and X-ray diffraction (XRD) and thermogravimetric analysis (TGA). The aim of the presented study was to improve the biological activity of curcumin by applying it to hierarchical zeolites based on FAU zeolite. The results showed that the loading efficiency of curcumin into hierarchical zeolites based on commercial FAU-type zeolite is enhanced by modifying the zeolite carrier itself. The hierarchical zeolites proved to be very good and efficient carriers of plant-derived active ingredients such as curcumin. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=carriers%20of%20active%20substances" title="carriers of active substances">carriers of active substances</a>, <a href="https://publications.waset.org/abstracts/search?q=curcumin" title=" curcumin"> curcumin</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20zeolites" title=" hierarchical zeolites"> hierarchical zeolites</a>, <a href="https://publications.waset.org/abstracts/search?q=incorporation" title=" incorporation"> incorporation</a> </p> <a href="https://publications.waset.org/abstracts/149020/hierarchical-zeolites-as-potential-carriers-of-curcumin" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149020.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">98</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">548</span> Efficient Subgoal Discovery for Hierarchical Reinforcement Learning Using Local Computations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adrian%20Millea">Adrian Millea</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In hierarchical reinforcement learning, one of the main issues encountered is the discovery of subgoal states or options (which are policies reaching subgoal states) by partitioning the environment in a meaningful way. This partitioning usually requires an expensive global clustering operation or eigendecomposition of the Laplacian of the states graph. We propose a local solution to this issue, much more efficient than algorithms using global information, which successfully discovers subgoal states by computing a simple function, which we call heterogeneity for each state as a function of its neighbors. Moreover, we construct a value function using the difference in heterogeneity from one step to the next, as reward, such that we are able to explore the state space much more efficiently than say epsilon-greedy. The same principle can then be applied to higher level of the hierarchy, where now states are subgoals discovered at the level below. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exploration" title="exploration">exploration</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20reinforcement%20learning" title=" hierarchical reinforcement learning"> hierarchical reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=locality" title=" locality"> locality</a>, <a href="https://publications.waset.org/abstracts/search?q=options" title=" options"> options</a>, <a href="https://publications.waset.org/abstracts/search?q=value%20functions" title=" value functions"> value functions</a> </p> <a href="https://publications.waset.org/abstracts/134077/efficient-subgoal-discovery-for-hierarchical-reinforcement-learning-using-local-computations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134077.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">171</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">547</span> Development of Antibacterial Surface Based on Bio-Inspired Hierarchical Surface</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.Ayazi">M.Ayazi</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Golshan%20Ebrahimi"> N. Golshan Ebrahimi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The development of antibacterial surface has devoted extensive researches and important field due to the growing antimicrobial resistance strains. The superhydrophobic surface has raised attention because of reducing bacteria adhesion in the absence of antibiotic agents. Evaluating the current development antibacterial surface has to be investigating to consider the potential of applying superhydrophobic surface to reduce bacterial adhesion or role of patterned surfaces on it. In this study, we present different samples with bio-inspired hierarchical and microstructures to consider their ability in reducing bacterial adhesion. The structures have inspired from rice-like pattern and lotus-leaf that developed on the polydimethylsiloxane (PDMS) and polypropylene (PP). The results of the attachment behaviors have considered on two bacteria strains of gram-negative Escherichia coli (E. coli) bacteria and gram-positive Staphylococcus aureus (S. aureus). The reduction of bacteria adhesion on these roughness surfaces demonstrated the effectiveness of rinsing ability on removing bacterial cells on structured plastic surfaces. Results have also offered the important role of bacterial species, material chemistry and hierarchical structure to prevent bacterial adhesion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20structure" title="hierarchical structure">hierarchical structure</a>, <a href="https://publications.waset.org/abstracts/search?q=self-cleaning" title=" self-cleaning"> self-cleaning</a>, <a href="https://publications.waset.org/abstracts/search?q=lotus-effect" title=" lotus-effect"> lotus-effect</a>, <a href="https://publications.waset.org/abstracts/search?q=bactericidal" title=" bactericidal"> bactericidal</a> </p> <a href="https://publications.waset.org/abstracts/98682/development-of-antibacterial-surface-based-on-bio-inspired-hierarchical-surface" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98682.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">136</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">546</span> Image Segmentation Using 2-D Histogram in RGB Color Space in Digital Libraries </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=El%20Asnaoui%20Khalid">El Asnaoui Khalid</a>, <a href="https://publications.waset.org/abstracts/search?q=Aksasse%20Brahim"> Aksasse Brahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Ouanan%20Mohammed"> Ouanan Mohammed </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an unsupervised color image segmentation method. It is based on a hierarchical analysis of 2-D histogram in RGB color space. This histogram minimizes storage space of images and thus facilitates the operations between them. The improved segmentation approach shows a better identification of objects in a color image and, at the same time, the system is fast. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title="image segmentation">image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20analysis" title=" hierarchical analysis"> hierarchical analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=2-D%20histogram" title=" 2-D histogram"> 2-D histogram</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/42096/image-segmentation-using-2-d-histogram-in-rgb-color-space-in-digital-libraries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42096.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">380</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">545</span> A Reactive Fast Inter-MAP Handover for Hierarchical Mobile IPv6</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pyung%20Soo%20Kim">Pyung Soo Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes an optimized reactive fast intermobility anchor point (MAP) handover scheme for Hierarchical Mobile IPv6, called the ORFH-HMIPv6, to minimize packet loss of the existing scheme. The key idea of the proposed ORFHHMIPv6 scheme is that the serving MAP buffers packets toward the mobile node (MN) as soon as the link layer between MN and serving base station is disconnected. To implement the proposed scheme, the MAP discovery message exchanged between MN and serving MAP is extended. In addition, the IEEE 802.21 Media Independent Handover Function (MIHF) event service message is defined newly. Through analytic performance evaluation, the proposed ORFH-HMIPv6 scheme can be shown to minimize packet loss much than the existing scheme. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20mobile%20IPv6%20%28HMIPv6%29" title="hierarchical mobile IPv6 (HMIPv6)">hierarchical mobile IPv6 (HMIPv6)</a>, <a href="https://publications.waset.org/abstracts/search?q=fast%20handover" title=" fast handover"> fast handover</a>, <a href="https://publications.waset.org/abstracts/search?q=reactive%20behavior" title=" reactive behavior"> reactive behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=packet%20loss" title=" packet loss"> packet loss</a> </p> <a href="https://publications.waset.org/abstracts/47084/a-reactive-fast-inter-map-handover-for-hierarchical-mobile-ipv6" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47084.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">213</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=hierarchical%20tag%20set&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=hierarchical%20tag%20set&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=hierarchical%20tag%20set&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=hierarchical%20tag%20set&page=5">5</a></li> <li class="page-item"><a class="page-link" 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