Relevant bibliographies by topics / Graph-based application

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  2. Dissertations / Theses
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  5. Conference papers
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Academic literature on the topic 'Graph-based application'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Graph-based application"

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FOGGIA, PASQUALE, GENNARO PERCANNELLA, CARLO SANSONE, and MARIO VENTO. "A GRAPH-BASED ALGORITHM FOR CLUSTER DETECTION." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 05 (August 2008): 843–60. http://dx.doi.org/10.1142/s0218001408006557.

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In some Computer Vision applications there is the need for grouping, in one or more clusters, only a part of the whole dataset. This happens, for example, when samples of interest for the application at hand are present together with several noisy samples. In this paper we present a graph-based algorithm for cluster detection that is particularly suited for detecting clusters of any size and shape, without the need of specifying either the actual number of clusters or the other parameters. The algorithm has been tested on data coming from two different computer vision applications. A comparison with other four state-of-the-art graph-based algorithms was also provided, demonstrating the effectiveness of the proposed approach.
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Padole, Himanshu, Shiv Dutt Joshi, and Tapan K. Gandhi. "Graph Wavelet-Based Multilevel Graph Coarsening and Its Application in Graph-CNN for Alzheimer’s Disease Detection." IEEE Access 8 (2020): 60906–17. http://dx.doi.org/10.1109/access.2020.2983590.

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Guan, Jun, Huiying Liu, Baolei Mao, and Xu Jiang. "Android Malware Detection Based on API Pairing." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 5 (October 2020): 965–70. http://dx.doi.org/10.1051/jnwpu/20203850965.

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Aiming at the problem that the permission-based detection is too coarse-grained, a malware detection method based on sensitive application program interface(API) pairing is proposed. The method decompiles the application to extract the sensitive APIs corresponding to the dangerous permissions, and uses the pairing of the sensitive APIs to construct the undirected graph of malicious applications and undirected graph of benign applications. According to the importance of sensitive APIs in malware and benign applications, different weights on the same edge in the different graphs are assigned to detect Android malicious applications. Experimental results show that the proposed method can effectively detect Android malicious applications and has practical significance.
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Zhu, Junxiang, Heap-Yih Chong, Hongwei Zhao, Jeremy Wu, Yi Tan, and Honglei Xu. "The Application of Graph in BIM/GIS Integration." Buildings 12, no. 12 (December 7, 2022): 2162. http://dx.doi.org/10.3390/buildings12122162.

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Information exchange between building information modelling (BIM) and geographic information system (GIS) is problematic, especially in terms of semantic information. Graph-based technologies, such as the resource description framework (RDF) and the labelled property graph (LPG), are promising in solving this problem. These two technologies are different but have not been systematically investigated in the context of BIM/GIS integration. This paper presents our systematic investigation into these two technologies, trying to propose the proper one for BIM/GIS data integration. The main findings are as follows. (1) Both LPG-based databases and RDF-based databases can be generally considered graph databases, but an LPG-based database is considered a native graph database, while an RDF-based database is not. (2) RDF suits applications focusing more on linking data and sharing data, and (3) LPG-based graph database suits applications focusing more on data query and analysis. An LPG-based graph database is thus proposed for BIM/GIS data integration. This review can facilitate the use of graph technology in BIM/GIS integration.
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HOLZRICHTER, MICHAEL, and SUELY OLIVEIRA. "A GRAPH BASED DAVIDSON ALGORITHM FOR THE GRAPH PARTITIONING PROBLEM." International Journal of Foundations of Computer Science 10, no. 02 (June 1999): 225–46. http://dx.doi.org/10.1142/s0129054199000162.

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The problem of partitioning a graph such that the number of edges incident to vertices in different partitions is minimized, arises in many contexts. Some examples include its recursive application for minimizing fill-in in matrix factorizations and load-balancing for parallel algorithms. Spectral graph partitioning algorithms partition a graph using the eigenvector associated with the second smallest eigenvalue of a matrix called the graph Laplacian. The focus of this paper is the use graph theory to compute this eigenvector more quickly.
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Schmalstieg, Dieter, Gerhard Reitmayr, and Gerd Hesina. "Distributed Applications for Collaborative Three-Dimensional Workspaces." Presence: Teleoperators and Virtual Environments 12, no. 1 (February 2003): 52–67. http://dx.doi.org/10.1162/105474603763835332.

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This paper focuses on the distributed architecture of the collaborative threedimensional user interface management system, Studierstube. The system allows multiple users to experience a shared 3D workspace populated by multiple applications using see-through head-mounted displays or other presentation media such as projection systems. Building large, ubiquitous, or mobile workspaces requires distribution of applications over several hosts in varying and dynamic configurations. The system design is based on a distributed shared scene graph that alleviates the application programmer from explicitly considering distribution and that avoids a separation of graphical and application data. The idea of unifying all system data in the scene graph is taken to its logical consequence by implementing application instances as nodes in the scene graph. Through the distributed shared scene graph mechanism, consistency of scene graph replicas and the contained application nodes is assured. Dynamic configuration management is based on application migration between participating hosts and a spatial model of locales allowing dynamic workgroup management. We describe a number of experimental workspaces that demonstrate the use of these configuration management techniques.
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Wang, Jianghan, Zhu Qu, Yihan Hu, Qiyun Ling, Jingyi Yu, and Yushan Jiang. "Diagnosis and Treatment Knowledge Graph Modeling Application Based on Chinese Medical Records." Electronics 12, no. 16 (August 11, 2023): 3412. http://dx.doi.org/10.3390/electronics12163412.

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In this study, a knowledge graph of Chinese medical record data was constructed based on graph database technology. An entity extraction method based on natural language processing, disambiguation, and reorganization for Chinese medical records is proposed, and dictionaries of drugs and treatment plans are constructed. Examples of applications of the knowledge graph in diagnosis and treatment prediction are given. Experimentally, it is found that the knowledge graph based on the graph database is 116.7% faster than the traditional database in complex relational queries.
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Czerepicki, A. "Application of graph databases for transport purposes." Bulletin of the Polish Academy of Sciences Technical Sciences 64, no. 3 (September 1, 2016): 457–66. http://dx.doi.org/10.1515/bpasts-2016-0051.

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Abstract The article presents an innovative concept of applying graph databases in transport information systems. The model of a graph database has been presented together with implementation of data structures and search operations in a graph. The transformation concept of relational model to a graph data model has been developed. The schema of graph database has been proposed for public transport information system purposes. The realization methods have been illustrated by the use of search function based on the Cypher query language.
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Zhang, Zeyue. "The Application of Graph Embedding Based on Random Walk." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 474–79. http://dx.doi.org/10.54097/hset.v16i.2624.

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In the historical process of scientific development, computers have a lofty position, and in recent years, graph embedding algorithms and models are one of the most popular subjects. A large number of similar data structures are indistinguishable by humans, but graph embedding can quickly compare and analyze these data structures. Existing research on random walk-based graph embedding methods is very rich. In order to summarize and classify the status quo of the more mature classical models and compare and integrate them, many different classical models are discussed in this paper. Based on different models, the problems solved, algorithm ideas, strategies, advantages, and disadvantages of the models are discussed in detail, and the application performance of some models is evaluated. DeepWalk model, Node2Vec model, HARP model are three graph embedding models based on the classical random walk model. Calculations for different data can occur by generating different node sequences. The three most important models in attribute random walk models are TriDNR model, GraphRNA model and FEATHER model. The model that only targets the information data in the shallow network is no longer suitable for the rapidly developing network. Attribute random walk models can handle data in deeper networks. At the end of this paper, the full text is summarized and the future prospect of this field is made.
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Abd Rahman, Hayati, Azrina Ashaari, and Nur Azima Alya Narawi. "STORYTELLING APPLICATION BASED ON INTERACTIVE STORY GRAPH STRUCTURE (ISGS)." MALAYSIAN JOURNAL OF COMPUTING 6, no. 1 (March 9, 2021): 715. http://dx.doi.org/10.24191/mjoc.v6i1.10370.

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Storytelling is a process of conveying series of events and information in words, images, and sound. Conventionally, storytelling developers/writers will apply the linear narrative structure approach to deliver the stories. However, that approach has some limitations; users cannot determine the path to end the story. They have no option to choose how to end the story based on their way of storytelling. Therefore, this study is about applying an Interactive Story Graph Structure (ISGS) approach to storytelling. ISGS approach is a structure used in storytelling in which users can revert their decision when going through the storytelling application implemented during the development. After completing the storytelling prototype development, a survey was conducted to test users’ enjoyment level when using the prototype. The survey was divided into four constructs: expectation, ease of navigation, understanding, and satisfaction. There were 36 respondents, and the data were collected on a random basis. Based on the survey’s result, most users (90.28%) enjoyed the storytelling application. The storytelling prototype was developed using Adobe Animate Creative Cloud and has been distributed among the respondents randomly. The analysis was conducted to determine the findings, limitations, and recommendations for future project improvement based on the results obtained. This study’s outcome is the complete production of storytelling application, which is creative and interactive with ISGS.
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Dissertations / Theses on the topic "Graph-based application"

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Ferrer, Sumsi Miquel. "Theory and Algorithms on the Median Graph. Application to Graph-based Classification and Clustering." Doctoral thesis, Universitat Autònoma de Barcelona, 2008. http://hdl.handle.net/10803/5788.

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Donat un conjunt d'objectes, el concepte genèric de mediana està definit com l'objecte amb la suma de distàncies a tot el conjunt, més petita. Sovint, aquest concepte és usat per a obtenir el representant del conjunt.
En el reconeixement estructural de patrons, els grafs han estat usats normalment per a representar objectes complexos. En el domini dels grafs, el concepte de mediana és conegut com median graph. Potencialment, té les mateixes aplicacions que el concepte de mediana per poder ser usat com a representant d'un conjunt de grafs.
Tot i la seva simple definició i les potencials aplicacions, s'ha demostrat que el seu càlcul és una tasca extremadament complexa. Tots els algorismes existents només han estat capaços de treballar amb conjunts petits de grafs, i per tant, la seva aplicació ha estat limitada en molts casos a usar dades sintètiques sense significat real. Així, tot i el seu potencial, ha restat com un concepte eminentment teòric.
L'objectiu principal d'aquesta tesi doctoral és el d'investigar a fons la teoria i l'algorísmica relacionada amb el concepte de medinan graph, amb l'objectiu final d'extendre la seva aplicabilitat i lliurar tot el seu potencial al món de les aplicacions reals. Per això, presentem nous resultats teòrics i també nous algorismes per al seu càlcul. Des d'un punt de vista teòric aquesta tesi fa dues aportacions fonamentals. Per una banda, s'introdueix el nou concepte d'spectral median graph. Per altra banda es mostra que certes de les propietats teòriques del median graph poden ser millorades sota determinades condicions. Més enllà de les aportacioncs teòriques, proposem cinc noves alternatives per al seu càlcul. La primera d'elles és una conseqüència directa del concepte d'spectral median graph. Després, basats en les millores de les propietats teòriques, presentem dues alternatives més per a la seva obtenció. Finalment, s'introdueix una nova tècnica per al càlcul del median basat en el mapeig de grafs en espais de vectors, i es proposen dos nous algorismes més.
L'avaluació experimental dels mètodes proposats utilitzant una base de dades semi-artificial (símbols gràfics) i dues amb dades reals (mollècules i pàgines web), mostra que aquests mètodes són molt més eficients que els existents. A més, per primera vegada, hem demostrat que el median graph pot ser un bon representant d'un conjunt d'objectes utilitzant grans quantitats de dades. Hem dut a terme experiments de classificació i clustering que validen aquesta hipòtesi i permeten preveure una pròspera aplicació del median graph a un bon nombre d'algorismes d'aprenentatge.
Given a set of objects, the generic concept of median is defined as the object with the smallest sum of distances to all the objects in the set. It has been often used as a good alternative to obtain a representative of the set.
In structural pattern recognition, graphs are normally used to represent structured objects. In the graph domain, the concept analogous to the median is known as the median graph. By extension, it has the same potential applications as the generic median in order to be used as the representative of a set of graphs.
Despite its simple definition and potential applications, its computation has been shown as an extremely complex task. All the existing algorithms can only deal with small sets of graphs, and its application has been constrained in most cases to the use of synthetic data with no real meaning. Thus, it has mainly remained in the box of the theoretical concepts.
The main objective of this work is to further investigate both the theory and the algorithmic underlying the concept of the median graph with the final objective to extend its applicability and bring all its potential to the world of real applications. To this end, new theory and new algorithms for its computation are reported. From a theoretical point of view, this thesis makes two main contributions. On one hand, the new concept of spectral median graph. On the other hand, we show that some of the existing theoretical properties of the median graph can be improved under some specific conditions. In addition to these theoretical contributions, we propose five new ways to compute the median graph. One of them is a direct consequence of the spectral median graph concept. In addition, we provide two new algorithms based on the new theoretical properties. Finally, we present a novel technique for the median graph computation based on graph embedding into vector spaces. With this technique two more new algorithms are presented.
The experimental evaluation of the proposed methods on one semi-artificial and two real-world datasets, representing graphical symbols, molecules and webpages, shows that these methods are much more ecient than the existing ones. In addition, we have been able to proof for the first time that the median graph can be a good representative of a class in large datasets. We have performed some classification and clustering experiments that validate this hypothesis and permit to foresee a successful application of the median graph to a variety of machine learning algorithms.
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Huang, Zan. "GRAPH-BASED ANALYSIS FOR E-COMMERCE RECOMMENDATION." Diss., Tucson, Arizona : University of Arizona, 2005. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1167%5F1%5Fm.pdf&type=application/pdf.

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Zhu, Ruifeng. "Contribution to graph-based manifold learning with application to image categorization." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCA015.

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Les algorithmes d'apprentissage de représentation de données à base de graphes sont considérés comme une technique puissante pour l'extraction de caractéristiques et la réduction de dimensionnalité dans les domaines de la reconnaissance de formes, la vision par ordinateur et l'apprentissage automatique. Ces algorithmes utilisent les informations contenues dans les similitudes d’échantillons (par paire) et la matrice du graphe pondéré pour révéler la structure géométrique intrinsèque de données. Ces algorithmes sont capables de récupérer une structure de faible dimension à partir de données de dimension élevée. Le travail de cette thèse consiste à développer des techniques d'apprentissage de représentation de données à base de graphes, appliquées à la reconnaissance de formes. Plus précisément, les expérimentations sont conduites sur des bases de données correspondant à plusieurs catégories d'images publiques telles que les bases de visages, les bases de scènes intérieures et extérieures, les bases d’objets, etc. Plusieurs approches sont proposées dans cette thèse : 1) Une nouvelle méthode non linéaire appelée inclusion discriminante flexible basée sur un graphe avec sélection de caractéristiques est proposée. Nous recherchons une représentation non linéaire et linéaire des données pouvant convenir à des tâches d'apprentissage génériques telles que la classification et le regroupement. En outre, un résultat secondaire de la méthode proposée est la sélection de caractéristiques originales, où la matrice de transformation linéaire estimée peut-être utilisée pour le classement et la sélection de caractéristiques. 2) Pour l'obtention d'une représentation non linéaire flexible et inductive des données, nous développons et étudions des stratégies et des algorithmes qui estiment simultanément la représentation de données désirée et une pondération explicite de caractéristiques. Le critère proposé estime explicitement les poids des caractéristiques ainsi que les données projetées et la transformation linéaire de sorte que la régularité des données et de grandes marges soient obtenues dans l'espace de projection. De plus, nous introduisons une variante à base de noyaux du modèle afin d'obtenir une représentation de données non linéaire inductive proche d'un véritable sous-espace non linéaire pour une bonne approximation des données. 3) Un apprentissage profond flexible qui peut surmonter les limites et les faiblesses des modèles d'apprentissage à une seule couche est introduit. Nous appelons cette stratégie une représentation basée sur un graphe élastique avec une architecture profonde qui explore en profondeur les informations structurelles des données. Le cadre résultant peut être utilisé pour les environnements semi-supervisés et supervisés. De plus, les problèmes d'optimisation qui en résultent peuvent être résolus efficacement. 4) Nous proposons une méthode semi-supervisée pour la représentation de données qui exploite la notion de convolution avec graphes. Cette méthode offre une nouvelle perspective de recherche sur la représentation de données non linéaires et établit un lien avec le traitement du signal sur les méthodes à base de graphes. La méthode proposée utilise et exploite les graphes de deux manières. Tout d'abord, il déploie une régularité des données sur les graphes. Deuxièmement, son modèle de régression est construit sur l'utilisation conjointe des données et de leur graphe en ce sens que le modèle de régression fonctionne avec des données convolutées. Ces dernières sont obtenues par propagation de caractéristiques
Graph-based Manifold Learning algorithms are regarded as a powerful technique for feature extraction and dimensionality reduction in Pattern Recogniton, Computer Vision and Machine Learning fields. These algorithms utilize sample information contained in the item-item similarity and weighted matrix to reveal the intrinstic geometric structure of manifold. It exhibits the low dimensional structure in the high dimensional data. This motivates me to develop Graph-based Manifold Learning techniques on Pattern Recognition, specially, application to image categorization. The experimental datasets of thesis correspond to several categories of public image datasets such as face datasets, indoor and outdoor scene datasets, objects datasets and so on. Several approaches are proposed in this thesis: 1) A novel nonlinear method called Flexible Discriminant graph-based Embedding with feature selection (FDEFS) is proposed. We seek a non-linear and a linear representation of the data that can be suitable for generic learning tasks such as classification and clustering. Besides, a byproduct of the proposed embedding framework is the feature selection of the original features, where the estimated linear transformation matrix can be used for feature ranking and selection. 2) We investigate strategies and related algorithms to develop a joint graph-based embedding and an explicit feature weighting for getting a flexible and inductive nonlinear data representation on manifolds. The proposed criterion explicitly estimates the feature weights together with the projected data and the linear transformation such that data smoothness and large margins are achieved in the projection space. Moreover, this chapter introduces a kernel variant of the model in order to get an inductive nonlinear embedding that is close to a real nonlinear subspace for a good approximation of the embedded data. 3) We propose the graph convolution based semi-supervised Embedding (GCSE). It provides a new perspective to non-linear data embedding research, and makes a link to signal processing on graph methods. The proposed method utilizes and exploits graphs in two ways. First, it deploys data smoothness over graphs. Second, its regression model is built on the joint use of the data and their graph in the sense that the regression model works with convolved data. The convolved data are obtained by feature propagation. 4) A flexible deep learning that can overcome the limitations and weaknesses of single-layer learning models is introduced. We call this strategy an Elastic graph-based embedding with deep architecture which deeply explores the structural information of the data. The resulting framework can be used for semi-supervised and supervised settings. Besides, the resulting optimization problems can be solved efficiently
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Martineau, Maxime. "Deep learning onto graph space : application to image-based insect recognition." Thesis, Tours, 2019. http://www.theses.fr/2019TOUR4024.

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Le but de cette thèse est d'étudier la reconnaissance d'insectes comme un problème de reconnaissance des formes basé images. Bien que ce problème ait été étudié en profondeur au long des trois dernières décennies, un aspect reste selon nous toujours à expérimenter à ce jour : les approches profondes (deep learning). À cet effet, la première contribution de cette thèse consiste à déterminer la faisabilité de l'application des réseaux de neurones convolutifs profonds (CNN) au problème de reconnaissance d'images d'insectes. Les limitations majeures ont les suivantes: les images sont très rares et les cardinalités de classes sont hautement déséquilibrées. Pour atténuer ces limitations, le transfer learning et la pondération de la fonction de coûts ont été employés. Des méthodes basées graphes sont également proposées et testées. La première consiste en la conception d'un classificateur de graphes de type perceptron. Le second travail basé sur les graphes de cette thèse est la définition d'un opérateur de convolution pour construire un modèle de réseaux de neurones convolutifs s'appliquant sur les graphes (GCNN.) Le dernier chapitre de la thèse s'applique à utiliser les méthodes mentionnées précédemment à des problèmes de reconnaissance d'images d'insectes. Deux bases d'images sont ici proposées. Là première est constituée d'images prises en laboratoire sur arrière-plan constant. La seconde base est issue de la base ImageNet. Cette base est composée d'images prises en contexte naturel. Les CNN entrainés avec transfer learning sont les plus performants sur ces bases d'images
The goal of this thesis is to investigate insect recognition as an image-based pattern recognition problem. Although this problem has been extensively studied along the previous three decades, an element is to the best of our knowledge still to be experimented as of 2017: deep approaches. Therefore, a contribution is about determining to what extent deep convolutional neural networks (CNNs) can be applied to image-based insect recognition. Graph-based representations and methods have also been tested. Two attempts are presented: The former consists in designing a graph-perceptron classifier and the latter graph-based work in this thesis is on defining convolution on graphs to build graph convolutional neural networks. The last chapter of the thesis deals with applying most of the aforementioned methods to insect image recognition problems. Two datasets are proposed. The first one consists of lab-based images with constant background. The second one is generated by taking a ImageNet subset. This set is composed of field-based images. CNNs with transfer learning are the most successful method applied on these datasets
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Kim, Pilho. "E-model event-based graph data model theory and implementation /." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29608.

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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.
Committee Chair: Madisetti, Vijay; Committee Member: Jayant, Nikil; Committee Member: Lee, Chin-Hui; Committee Member: Ramachandran, Umakishore; Committee Member: Yalamanchili, Sudhakar. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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GRASSI, FRANCESCO. "Statistical and Graph-Based Signal Processing: Fundamental Results and Application to Cardiac Electrophysiology." Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2710580.

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The goal of cardiac electrophysiology is to obtain information about the mechanism, function, and performance of the electrical activities of the heart, the identification of deviation from normal pattern and the design of treatments. Offering a better insight into cardiac arrhythmias comprehension and management, signal processing can help the physician to enhance the treatment strategies, in particular in case of atrial fibrillation (AF), a very common atrial arrhythmia which is associated to significant morbidities, such as increased risk of mortality, heart failure, and thromboembolic events. Catheter ablation of AF is a therapeutic technique which uses radiofrequency energy to destroy atrial tissue involved in the arrhythmia sustenance, typically aiming at the electrical disconnection of the of the pulmonary veins triggers. However, recurrence rate is still very high, showing that the very complex and heterogeneous nature of AF still represents a challenging problem. Leveraging the tools of non-stationary and statistical signal processing, the first part of our work has a twofold focus: firstly, we compare the performance of two different ablation technologies, based on contact force sensing or remote magnetic controlled, using signal-based criteria as surrogates for lesion assessment. Furthermore, we investigate the role of ablation parameters in lesion formation using the late-gadolinium enhanced magnetic resonance imaging. Secondly, we hypothesized that in human atria the frequency content of the bipolar signal is directly related to the local conduction velocity (CV), a key parameter characterizing the substrate abnormality and influencing atrial arrhythmias. Comparing the degree of spectral compression among signals recorded at different points of the endocardial surface in response to decreasing pacing rate, our experimental data demonstrate a significant correlation between CV and the corresponding spectral centroids. However, complex spatio-temporal propagation pattern characterizing AF spurred the need for new signals acquisition and processing methods. Multi-electrode catheters allow whole-chamber panoramic mapping of electrical activity but produce an amount of data which need to be preprocessed and analyzed to provide clinically relevant support to the physician. Graph signal processing has shown its potential on a variety of applications involving high-dimensional data on irregular domains and complex network. Nevertheless, though state-of-the-art graph-based methods have been successful for many tasks, so far they predominantly ignore the time-dimension of data. To address this shortcoming, in the second part of this dissertation, we put forth a Time-Vertex Signal Processing Framework, as a particular case of the multi-dimensional graph signal processing. Linking together the time-domain signal processing techniques with the tools of GSP, the Time-Vertex Signal Processing facilitates the analysis of graph structured data which also evolve in time. We motivate our framework leveraging the notion of partial differential equations on graphs. We introduce joint operators, such as time-vertex localization and we present a novel approach to significantly improve the accuracy of fast joint filtering. We also illustrate how to build time-vertex dictionaries, providing conditions for efficient invertibility and examples of constructions. The experimental results on a variety of datasets suggest that the proposed tools can bring significant benefits in various signal processing and learning tasks involving time-series on graphs. We close the gap between the two parts illustrating the application of graph and time-vertex signal processing to the challenging case of multi-channels intracardiac signals.
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Bush, Stephen J. Baker Erich J. "Automated sequence homology using empirical correlations to create graph-based networks for the elucidation of protein relationships /." Waco, Tex. : Baylor University, 2008. http://hdl.handle.net/2104/5221.

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Zhu, Xiaoting. "Systematic Assessment of Structural Features-Based Graph Embedding Methods with Application to Biomedical Networks." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592394966493963.

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Zhang, Yan. "Improving the efficiency of graph-based data mining with application to public health data." Online access for everyone, 2007. http://www.dissertations.wsu.edu/Thesis/Fall2007/y_zhang_112907.pdf.

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Loureiro, Rui. "Bond graph model based on structural diagnosability and recoverability analysis : application to intelligent autonomous vehicles." Thesis, Lille 1, 2012. http://www.theses.fr/2012LIL10079/document.

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La présente thèse concerne l’étude structurelle pour le recouvrement du défaut par l’approche du bond graph. L'objectif est d'exploiter les propriétés structurelles et causales de l'outil bond graph, afin d’effectuer à la fois le diagnostic et l’analyse de la commande du système physique en présence du défaut. En effet, l’outil bond graph permet de vérifier les conditions structurelles de recouvrement de défauts pas seulement du point de vue de l’analyse de commande, mais aussi en considérant les informations issues de l’étape de diagnostic. Par conséquent, l’ensemble des défauts tolérés est obtenu en mode hors-ligne avant d’effectuer une implémentation réelle. En outre, en estimant le défaut comme une puissance perturbatrice fournie au système, ce qui permet d’étendre les résultats d’analyse structurelle pour le recouvrement du défaut à une compensation locale adaptative, directement à partir du modèle bond graph. Enfin, les résultats obtenus sont validés dans une application d’un véhicule autonome intelligent redondant
This work deals with structural fault recoverability analysis using the bond graph model. The objective is to exploit the structural and causal properties of the bond graph tool in order to perform both diagnosis and control analysis in the presence of faults. Indeed, the bond graph tool enables to verify the structural conditions of fault recoverability not only from a control perspective but also from a diagnosis one. In this way, the set of faults that can be recovered is obtained previous to industrial implementation. In addition, a novel way to estimate the fault by a disturbing power furnished to the system, enabled to extend the results of structural fault recoverability by performing a local adaptive compensation directly from the bond graph model. Finally, the obtained structural results are validated on a redundant intelligent autonomous vehicle
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Books on the topic "Graph-based application"

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Xiao, Bai, Jian Cheng, and Edwin R. Hancock. Graph-based methods in computer vision: Developments and applications. Hershey: Information Science Reference, 2012.

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Lee, Raymond Shu Tak. Invariant object recognition based on elastic graph matching: Theory and applications. Amsterdam: IOS Press, 2003.

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Österreichische Arbeitsgruppe für Mustererkennung. Tagung. Applications of 3D-imaging and graph-based modeling 2000: 24th Workshop of the Austrian Association for Pattern Recognition (ÖAGM/AAPR), Villach, Carinthia, Austria, May 25-26, 2000. Wien: Österreichische Computer Gesellschaft, 2000.

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Coolen, A. C. C., A. Annibale, and E. S. Roberts. Applications of random graphs. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0011.

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This chapter reviews graph generation techniques in the context of applications. The first case study is power grids, where proposed strategies to prevent blackouts have been tested on tailored random graphs. The second case study is in social networks. Applications of random graphs to social networks are extremely wide ranging – the particular aspect looked at here is modelling the spread of disease on a social network – and how a particular construction based on projecting from a bipartite graph successfully captures some of the clustering observed in real social networks. The third case study is on null models of food webs, discussing the specific constraints relevant to this application, and the topological features which may contribute to the stability of an ecosystem. The final case study is taken from molecular biology, discussing the importance of unbiased graph sampling when considering if motifs are over-represented in a protein–protein interaction network.
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Invariant Object Recognition Based on Elastic Graph Matching (Frontiers in Artificial Intelligence and Applications, 86). Ios Pr Inc, 2002.

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Heckel, Reiko, and Gabriele Taentzer. Graph Transformation for Software Engineers: With Applications to Model-Based Development and Domain-Specific Language Engineering. Springer International Publishing AG, 2021.

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Heckel, Reiko, and Gabriele Taentzer. Graph Transformation for Software Engineers: With Applications to Model-Based Development and Domain-Specific Language Engineering. Springer, 2020.

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Coolen, Ton, Alessia Annibale, and Ekaterina Roberts. Generating Random Networks and Graphs. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.001.0001.

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This book supports researchers who need to generate random networks, or who are interested in the theoretical study of random graphs. The coverage includes exponential random graphs (where the targeted probability of each network appearing in the ensemble is specified), growth algorithms (i.e. preferential attachment and the stub-joining configuration model), special constructions (e.g. geometric graphs and Watts Strogatz models) and graphs on structured spaces (e.g. multiplex networks). The presentation aims to be a complete starting point, including details of both theory and implementation, as well as discussions of the main strengths and weaknesses of each approach. It includes extensive references for readers wishing to go further. The material is carefully structured to be accessible to researchers from all disciplines while also containing rigorous mathematical analysis (largely based on the techniques of statistical mechanics) to support those wishing to further develop or implement the theory of random graph generation. This book is aimed at the graduate student or advanced undergraduate. It includes many worked examples, numerical simulations and exercises making it suitable for use in teaching. Explicit pseudocode algorithms are included to make the ideas easy to apply. Datasets are becoming increasingly large and network applications wider and more sophisticated. Testing hypotheses against properly specified control cases (null models) is at the heart of the ‘scientific method’. Knowledge on how to generate controlled and unbiased random graph ensembles is vital for anybody wishing to apply network science in their research.
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Book chapters on the topic "Graph-based application"

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Symeonidou, Danai, Isabelle Sanchez, Madalina Croitoru, Pascal Neveu, Nathalie Pernelle, Fatiha Saïs, Aurelie Roland-Vialaret, Patrice Buche, Aunur-Rofiq Muljarto, and Remi Schneider. "Key Discovery for Numerical Data: Application to Oenological Practices." In Graph-Based Representation and Reasoning, 222–36. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40985-6_17.

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Purchase, Helen C., David Carrington, and Jo-Anne Allder. "Experimenting with Aesthetics-Based Graph Layout." In Theory and Application of Diagrams, 498–501. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44590-0_46.

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Dibie, Juliette, Stéphane Dervaux, Estelle Doriot, Liliana Ibanescu, and Caroline Pénicaud. "$$[MS]^2O$$ – A Multi-scale and Multi-step Ontology for Transformation Processes: Application to Micro-Organisms." In Graph-Based Representation and Reasoning, 163–76. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40985-6_13.

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Deptuła, A. "Application of Game Graphs to Describe the Inverse Problem in the Designing of Mechatronic Vibrating Systems." In Graph-Based Modelling in Engineering, 189–99. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39020-8_14.

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Yu, Yangming, Zhiyong Zha, Bo Jin, Geng Wu, and Chenxi Dong. "Graph-Based Anomaly Detection via Attention Mechanism." In Intelligent Computing Theories and Application, 401–11. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13870-6_33.

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Ambauen, R., S. Fischer, and Horst Bunke. "Graph Edit Distance with Node Splitting and Merging, and Its Application to Diatom Identification." In Graph Based Representations in Pattern Recognition, 95–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45028-9_9.

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Dahm, Nicholas, Horst Bunke, Terry Caelli, and Yongsheng Gao. "A Unified Framework for Strengthening Topological Node Features and Its Application to Subgraph Isomorphism Detection." In Graph-Based Representations in Pattern Recognition, 11–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38221-5_2.

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Bislimovska, Bojana, Alessandro Bozzon, Marco Brambilla, and Piero Fraternali. "Graph-Based Search over Web Application Model Repositories." In Lecture Notes in Computer Science, 90–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22233-7_7.

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Kazemian, Hassan, Mohammad-Hossein Amirhosseini, and Michael Phillips. "Application of Graph-Based Technique to Identity Resolution." In IFIP Advances in Information and Communication Technology, 471–82. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08333-4_38.

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Wang, Ya, Guowen Pan, Jinwen Ma, Xiangchen Li, and Albert Zhong. "Label Similarity Based Graph Network for Badminton Activity Recognition." In Intelligent Computing Theories and Application, 557–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84522-3_46.

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Conference papers on the topic "Graph-based application"

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Lim, Jiyoun, and NamKyung Lee. "Graph Feature Generation based on Scene Graph Benchmark Application on Video." In 2022 13th International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2022. http://dx.doi.org/10.1109/ictc55196.2022.9952683.

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Tang, DeQuan, and Yang Tan. "Graph-Based Bioinformatics Mining Research and Application." In 2011 Fourth International Symposium on Knowledge Acquisition and Modeling (KAM). IEEE, 2011. http://dx.doi.org/10.1109/kam.2011.83.

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Xia, Chunwei, and Xin Wang. "Graph-Based Web Query Classification." In 2015 12th Web Information System and Application Conference (WISA). IEEE, 2015. http://dx.doi.org/10.1109/wisa.2015.68.

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Joo, Hanbyul, Yekeun Jeong, Olivier Duchenne, Seong-Young Ko, and In-So Kweon. "Graph-based robust shape matching for robotic application." In 2009 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2009. http://dx.doi.org/10.1109/robot.2009.5152594.

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Zhi, Huilai, and Zongtian Liu. "Event Importance Analysis Based on Directed Graph." In 2008 International Symposium on Intelligent Information Technology Application Workshops. IEEE, 2008. http://dx.doi.org/10.1109/iita.workshops.2008.140.

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Wang, Kai, and Danwei Chen. "Graph Structure Based Anomaly Behavior Detection." In 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/iccia-17.2017.90.

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Chen, Xiaoping, Jianfeng Wang, Hong Zhang, and Qingjie Hu. "Knowledge Encapsulation and Application Based on Domain Knowledge Graph." In 2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE, 2023. http://dx.doi.org/10.1109/eebda56825.2023.10090622.

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You, Chang, Lawrence Holder, and Diane Cook. "Application of Graph-based Data Mining to Metabolic Pathways." In Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06). IEEE, 2006. http://dx.doi.org/10.1109/icdmw.2006.31.

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Deng, Li-Qiong, Gui-Xin Zhang, and Yuan Ren. "Image Semantic Analysis and Application Based on Knowledge Graph." In 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2018. http://dx.doi.org/10.1109/cisp-bmei.2018.8633063.

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Shao, Yinning, Yukai Zhao, Hang Yu, Min Liu, and Yunlong Ma. "Graph Pooling based Human Detection Method for Industrial Application." In 2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2023. http://dx.doi.org/10.1109/cscwd57460.2023.10152547.

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Reports on the topic "Graph-based application"

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Patwardhan, Kedar A., Guillermo Sapiro, and Vassilios Morellas. A Graph-based Foreground Representation and Its Application in Example Based People Matching in Video (PREPRINT). Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada478409.

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Wan, Wei. A New Approach to the Decomposition of Incompletely Specified Functions Based on Graph Coloring and Local Transformation and Its Application to FPGA Mapping. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6582.

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Morin, Shai, Gregory Walker, Linda Walling, and Asaph Aharoni. Identifying Arabidopsis thaliana Defense Genes to Phloem-feeding Insects. United States Department of Agriculture, February 2013. http://dx.doi.org/10.32747/2013.7699836.bard.

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The whitefly (Bemisia tabaci) is a serious agricultural pest that afflicts a wide variety of ornamental and vegetable crop species. To enable survival on a great diversity of host plants, whiteflies must have the ability to avoid or detoxify numerous different plant defensive chemicals. Such toxins include a group of insect-deterrent molecules called glucosinolates (GSs), which also provide the pungent taste of Brassica vegetables such as radish and cabbage. In our BARD grant, we used the whitefly B. tabaci and Arabidopsis (a Brassica plant model) defense mutants and transgenic lines, to gain comprehensive understanding both on plant defense pathways against whiteflies and whitefly defense strategies against plants. Our major focus was on GSs. We produced transgenic Arabidopsis plants accumulating high levels of GSs. At the first step, we examined how exposure to high levels of GSs affects decision making and performance of whiteflies when provided plants with normal levels or high levels of GSs. Our major conclusions can be divided into three: (I) exposure to plants accumulating high levels of GSs, negatively affected the performance of both whitefly adult females and immature; (II) whitefly adult females are likely to be capable of sensing different levels of GSs in their host plants and are able to choose, for oviposition, the host plant on which their offspring survive and develop better (preference-performance relationship); (III) the dual presence of plants with normal levels and high levels of GSs, confused whitefly adult females, and led to difficulties in making a choice between the different host plants. These findings have an applicative perspective. Whiteflies are known as a serious pest of Brassica cropping systems. If the differences found here on adjacent small plants translate to field situations, intercropping with closely-related Brassica cultivars could negatively influence whitefly population build-up. At the second step, we characterized the defensive mechanisms whiteflies use to detoxify GSs and other plant toxins. We identified five detoxification genes, which can be considered as putative "key" general induced detoxifiers because their expression-levels responded to several unrelated plant toxic compounds. This knowledge is currently used (using new funding) to develop a new technology that will allow the production of pestresistant crops capable of protecting themselves from whiteflies by silencing insect detoxification genes without which successful host utilization can not occur. Finally, we made an effort to identify defense genes that deter whitefly performance, by infesting with whiteflies, wild-type and defense mutated Arabidopsis plants. The infested plants were used to construct deep-sequencing expression libraries. The 30- 50 million sequence reads per library, provide an unbiased and quantitative assessment of gene expression and contain sequences from both Arabidopsis and whiteflies. Therefore, the libraries give us sequence data that can be mined for both the plant and insect gene expression responses. An intensive analysis of these datasets is underway. We also conducted electrical penetration graph (EPG) recordings of whiteflies feeding on Arabidopsis wild-type and defense mutant plants in order to determine the time-point and feeding behavior in which plant-defense genes are expressed. We are in the process of analyzing the recordings and calculating 125 feeding behavior parameters for each whitefly. From the analyses conducted so far we conclude that the Arabidopsis defense mutants do not affect adult feeding behavior in the same manner that they affect immatures development. Analysis of the immatures feeding behavior is not yet completed, but if it shows the same disconnect between feeding behavior data and developmental rate data, we would conclude that the differences in the defense mutants are due to a qualitative effect based on the chemical constituency of the phloem sap.
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