Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Graph-type classification.
Статті в журналах з теми "Graph-type classification"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-50 статей у журналах для дослідження на тему "Graph-type classification".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Переглядайте статті в журналах для різних дисциплін та оформлюйте правильно вашу бібліографію.
1
Perepelitsa, V. A., I. V. Kozin, and S. V. Kurapov. "Methods of classification and algorithms of graph coloring." Researches in Mathematics 16 (February 7, 2021): 135. http://dx.doi.org/10.15421/240816.
Повний текст джерелаАнотація:
We study the connection between classifications on finite set and the problem of graph coloring. We consider the optimality criterion for classification of special type: h-classifications, which are built on the base of proximity measure. It is shown that the problem of finding the optimal h-classification can be reduced to the problem of coloring of non-adjacency graph vertices by the smallest possible number of colors. We consider algorithms of proper coloring of graph vertices.
2
Gharaee, Zahra, Shreyas Kowshik, Oliver Stromann, and Michael Felsberg. "Graph representation learning for road type classification." Pattern Recognition 120 (December 2021): 108174. http://dx.doi.org/10.1016/j.patcog.2021.108174.
Повний текст джерела
3
Yang, Dilian. "Factoriality and Type Classification of k-Graph von Neumann Algebras." Proceedings of the Edinburgh Mathematical Society 60, no. 2 (2016): 499–518. http://dx.doi.org/10.1017/s0013091516000304.
Повний текст джерелаАнотація:
AbstractLet be a single vertex k-graph and let be the von Neumann algebra induced from the Gelfand–Naimark–Segal (GNS) representation of a distinguished state ω of its k-graph C*-algebra . In this paper we prove the factoriality of , and furthermore determine its type when either has the little pullback property, or the intrinsic group of has rank 0. The key step to achieving this is to show that the fixed-point algebra of the modular action corresponding to ω has a unique tracial state.
4
Power, Stephen C., Igor A. Baburin, and Davide M. Proserpio. "Isotopy classes for 3-periodic net embeddings." Acta Crystallographica Section A Foundations and Advances 76, no. 3 (2020): 275–301. http://dx.doi.org/10.1107/s2053273320000625.
Повний текст джерелаАнотація:
Entangled embedded periodic nets and crystal frameworks are defined, along with their dimension type, homogeneity type, adjacency depth and periodic isotopy type. Periodic isotopy classifications are obtained for various families of embedded nets with small quotient graphs. The 25 periodic isotopy classes of depth-1 embedded nets with a single-vertex quotient graph are enumerated. Additionally, a classification is given of embeddings of n-fold copies of pcu with all connected components in a parallel orientation and n vertices in a repeat unit, as well as demonstrations of their maximal symmetry periodic isotopes. The methodology of linear graph knots on the flat 3-torus [0,1)3 is introduced. These graph knots, with linear edges, are spatial embeddings of the labelled quotient graphs of an embedded net which are associated with its periodicity bases.
5
Wang, Zijian, Rafael Sacks, and Timson Yeung. "Exploring graph neural networks for semantic enrichment: Room type classification." Automation in Construction 134 (February 2022): 104039. http://dx.doi.org/10.1016/j.autcon.2021.104039.
Повний текст джерела
6
Rowland, Dana. "Classification of book representations of K6." Journal of Knot Theory and Its Ramifications 26, no. 12 (2017): 1750075. http://dx.doi.org/10.1142/s0218216517500754.
Повний текст джерелаАнотація:
A book representation of a graph is a particular way of embedding a graph in three-dimensional space so that the vertices lie on a circle and the edges are chords on disjoint topological disks. We describe a set of operations on book representations that preserves ambient isotopy, and apply these operations to [Formula: see text], the complete graph with six vertices. We prove there are exactly 59 distinct book representations for [Formula: see text], and we identify the number and type of knotted and linked cycles in each representation. We show that book representations of [Formula: see text] contain between one and seven links, and up to nine knotted cycles. Furthermore, all links and cycles in a book representation of [Formula: see text] have crossing number at most four.
7
Dong, Guozhong, Weizhe Zhang, Rahul Yadav, Xin Mu, and Zhili Zhou. "OWGC-HMC: An Online Web Genre Classification Model Based on Hierarchical Multilabel Classification." Security and Communication Networks 2022 (March 29, 2022): 1–9. http://dx.doi.org/10.1155/2022/7549880.
Повний текст джерелаАнотація:
Web genre plays an important role in focused crawling, web link analysis, and contextual advertising. In this paper, web genre is defined as the functional purpose and the information type contained in the website. The intelligent classification of web genre can predict the content and functional type of website. However, there are several critical challenges to solve the web genre classification problem: lack of web genre classification dataset and efficient web genre classification mechanism. To improve web genre classification performance, we crawled Chinese websites of different web genres and converted crawled data into a hierarchical multilabel classification dataset. A website knowledge graph is constructed based on the relationship of website and meta tag features. Using entity features extracted from the knowledge graph, we propose an online web genre classification model based on hierarchical multilabel classification (OWGC-HMC) to mine the functional purpose of the corresponding website. Experimental results show that our OWGC-HMC model can mine hierarchical multilabel structure of web genre and outperform other web genre classification methods.
8
Chen, Yang, Zhonglin Ye, Haixing Zhao, and Ying Wang. "Feature-Based Graph Backdoor Attack in the Node Classification Task." International Journal of Intelligent Systems 2023 (February 21, 2023): 1–13. http://dx.doi.org/10.1155/2023/5418398.
Повний текст джерелаАнотація:
Graph neural networks (GNNs) have shown significant performance in various practical applications due to their strong learning capabilities. Backdoor attacks are a type of attack that can produce hidden attacks on machine learning models. GNNs take backdoor datasets as input to produce an adversary-specified output on poisoned data but perform normally on clean data, which can have grave implications for applications. Backdoor attacks are under-researched in the graph domain, and almost existing graph backdoor attacks focus on the graph-level classification task. To close this gap, we propose a novel graph backdoor attack that uses node features as triggers and does not need knowledge of the GNNs parameters. In the experiments, we find that feature triggers can destroy the feature spaces of the original datasets, resulting in GNNs inability to identify poisoned data and clean data well. An adaptive method is proposed to improve the performance of the backdoor model by adjusting the graph structure. We conducted extensive experiments to validate the effectiveness of our model on three benchmark datasets.
9
Li, Yiyan, Xiaomin Lu, Haowen Yan, Wenning Wang, and Pengbo Li. "A Skeleton-Line-Based Graph Convolutional Neural Network for Areal Settlements’ Shape Classification." Applied Sciences 12, no. 19 (2022): 10001. http://dx.doi.org/10.3390/app121910001.
Повний текст джерелаАнотація:
Among the geographic elements, shape recognition and classification is one of the im portant elements of map cartographic generalization, and the shape classification of an areal settlement is an important part of geospatial vector data. However, there is currently no relatively simple and efficient way to achieve areal settlement classification. Therefore, we combined the skeleton line vector data of an areal settlement and the graph convolutional neural network to propose an areal settlement shape classification method that (1) extracts the skeleton line of the areal settlement to form a dual graph with nodes as edges, (2) extracts multiple features to obtain a graph representation of the shape, (3) extracts and aggregates the shape information represented by the areal settlement skeleton line using the graph convolutional neural network for multiple rounds to extract high-dimensional shape information, and (4) completes the shape classification of the high-dimensional shape information. The experiment used 240 samples, and the classification accuracy was 93.3%, with areal settlement shapes of E-, F-, and H-type achieving F-measures of 96.5%, 92.3%, and 100%, respectively. The result shows that the classification method of the areal settlement shape has high accuracy.
10
TAO, PENG, CAO WENLI, CHEN JIA, et al. "Research on fabric classification based on graph neural network." Industria Textila 74, no. 01 (2023): 3–11. http://dx.doi.org/10.35530/it.074.01.202224.
Повний текст джерелаАнотація:
Fabric classification plays a crucial role in the modern textile industry and fashion market. In the early stage, traditional neural network methods were adopted to identify fabrics with the drawback of restricted fabric type and poor accuracy. Combining multi-frame temporality and analysing fabric graph data made from fabric motion features, this paper proposes a novel hybrid model that introduces the concept of graph networks to classify 30 textile materials in a public database. We utilize the graph inductive representation learning method (GraphSAGE, Graph Sample and Aggregate) to extract node embedding features of the fabric. Moreover, bidirectional gated recurrent unit and layer attention mechanism (BiGRU-attention) are employed in the last layer of aggregation to calculate the score of previous cells. Intending to further enhance performance, we link the jump connection with adaptive selection aggregation frameworks to determine the influential region of each node. Our method breaks through the limitation that the original methods can only classify a few fabrics with great classification results.
11
Bongini, Pietro, Niccolò Pancino, Asma Bendjeddou, Franco Scarselli, Marco Maggini, and Monica Bianchini. "Composite Graph Neural Networks for Molecular Property Prediction." International Journal of Molecular Sciences 25, no. 12 (2024): 6583. http://dx.doi.org/10.3390/ijms25126583.
Повний текст джерелаАнотація:
Graph Neural Networks have proven to be very valuable models for the solution of a wide variety of problems on molecular graphs, as well as in many other research fields involving graph-structured data. Molecules are heterogeneous graphs composed of atoms of different species. Composite graph neural networks process heterogeneous graphs with multiple-state-updating networks, each one dedicated to a particular node type. This approach allows for the extraction of information from s graph more efficiently than standard graph neural networks that distinguish node types through a one-hot encoded type of vector. We carried out extensive experimentation on eight molecular graph datasets and on a large number of both classification and regression tasks. The results we obtained clearly show that composite graph neural networks are far more efficient in this setting than standard graph neural networks.
12
Eilers, Søren, Gunnar Restorff, Efren Ruiz, and Adam P. W. Sørensen. "Geometric Classification of Graph C*-algebras over Finite Graphs." Canadian Journal of Mathematics 70, no. 2 (2018): 294–353. http://dx.doi.org/10.4153/cjm-2017-016-7.
Повний текст джерелаАнотація:
AbstractWe address the classification problem for graph C*-algebras of finite graphs (finitely many edges and vertices), containing the class of Cuntz-Krieger algebras as a prominent special case. Contrasting earlier work, we do not assume that the graphs satisfy the standard condition (K), so that the graph C*-algebras may come with uncountably many ideals.We find that in this generality, stable isomorphism of graph C*-algebras does not coincide with the geometric notion of Cuntz move equivalence. However, adding a modest condition on the graphs, the two notions are proved to be mutually equivalent and equivalent to the C*-algebras having isomorphicK-theories. This proves in turn that under this condition, the graph C*-algebras are in fact classifiable byK-theory, providing, in particular, complete classification when the C* - algebras in question are either of real rank zero or type I/postliminal. The key ingredient in obtaining these results is a characterization of Cuntz move equivalence using the adjacency matrices of the graphs.Our results are applied to discuss the classification problem for the quantumlens spaces defined by Hong and Szymański, and to complete the classification of graph C*-algebras associated with all simple graphs with four vertices or less.
13
Williams, Gerald. "Fibonacci Type Semigroups." Algebra Colloquium 21, no. 04 (2014): 647–52. http://dx.doi.org/10.1142/s1005386714000595.
Повний текст джерелаАнотація:
We study “Fibonacci type” groups and semigroups. By establishing asphericity of their presentations we show that many of the groups are infinite. We combine this with Adjan graph techniques and the classification of the finite Fibonacci semigroups (in terms of the finite Fibonacci groups) to extend it to the Fibonacci type semigroups.
14
Yang, Tianchi, Linmei Hu, Chuan Shi, Houye Ji, Xiaoli Li, and Liqiang Nie. "HGAT: Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification." ACM Transactions on Information Systems 39, no. 3 (2021): 1–29. http://dx.doi.org/10.1145/3450352.
Повний текст джерелаАнотація:
Short text classification has been widely explored in news tagging to provide more efficient search strategies and more effective search results for information retrieval. However, most existing studies, concentrating on long text classification, deliver unsatisfactory performance on short texts due to the sparsity issue and the insufficiency of labeled data. In this article, we propose a novel heterogeneous graph neural network-based method for semi-supervised short text classification, leveraging full advantage of limited labeled data and large unlabeled data through information propagation along the graph. Specifically, we first present a flexible heterogeneous information network (HIN) framework for modeling short texts, which can integrate any type of additional information and meanwhile capture their relations to address the semantic sparsity. Then, we propose Heterogeneous Graph Attention networks (HGAT) to embed the HIN for short text classification based on a dual-level attention mechanism, including node-level and type-level attentions. To efficiently classify new coming texts that do not previously exist in the HIN, we extend our model HGAT for inductive learning, avoiding re-training the model on the evolving HIN. Extensive experiments on single-/multi-label classification demonstrates that our proposed model HGAT significantly outperforms state-of-the-art methods across the benchmark datasets under both transductive and inductive learning.
15
Sarunya, Kanjanawattana, and Kimura Masaomi. "BRAIN Journal - ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation." BRAIN: Broad Research in Artificial Intelligence and Neuroscience 8, no. 2 (2017): 5–25. https://doi.org/10.5281/zenodo.1045375.
Повний текст джерелаАнотація:
ABSTRACT Image classification plays a vital role in many areas of study, such as data mining and image processing; however, serious problems collectively referred to as the course of dimensionality have been encountered in previous studies as factors that reduce system performance. Furthermore, we also confront the problem of different graph characteristics even if graphs belong to same types. In this study, we propose a novel method of graph-type classification. Using our approach, we open up a new solution of high-dimensional images and address problems of different characteristics by converting graph images to one dimension with a discrete Fourier transformation and creating numeric datasets using wavelet and Hough transformations. Moreover, we introduce a new classifier, which is a combination between artificial neuron networks (ANNs) and support vector machines (SVMs), which we call ANNSVM, to enhance accuracy. The objectives of our study are to propose an effective graph-type classification method that includes finding a new data representative used for classification instead of two-dimensional images and to investigate what features make our data separable. To evaluate the method of our study, we conducted five experiments with different methods and datasets. The input dataset we focused on was a numeric dataset containing wavelet coefficients and outputs of a Hough transformation. From our experimental results, we observed that the highest accuracy was provided using our method with Coiflet 1, which achieved a 0.91 accuracy.
16
Gopalakrishnan, Atul Anand, Jakir Hossain, Tuğrulcan Elmas, and Ahmet Erdem Sariyüce. "Large Engagement Networks for Classifying Coordinated Campaigns and Organic Twitter Trends." Proceedings of the International AAAI Conference on Web and Social Media 19 (June 7, 2025): 688–702. https://doi.org/10.1609/icwsm.v19i1.35839.
Повний текст джерелаАнотація:
Social media users and inauthentic accounts, such as bots, may coordinate in promoting their topics. Such topics may give the impression that they are organically popular among the public, even though they are astroturfing campaigns that are centrally managed. It is challenging to predict if a topic is organic or a coordinated campaign due to the lack of reliable ground truth. In this paper, we create such a ground truth by detecting the campaigns promoted by ephemeral astroturfing attacks. These attacks push any topic to Twitter’s (X) trends list by employing bots that tweet in a coordinated manner within a short period and then immediately delete their tweets. We also manually curate a dataset of organic Twitter trends. We then create engagement networks out of these datasets which can serve as a challenging testbed for graph classification task to distinguish between campaigns and organic trends. Engagement networks consist of users as nodes and edges indicate engagements (retweets, replies, and quotes) between users. We release the engagement networks for 179 campaigns and 135 non-campaigns, and also provide finer-grain labels to characterize the type of the campaigns and non-campaigns. Our dataset, LEN (Large Engagement Networks), in the URL below. In comparison to traditional graph classification datasets, which are small with tens of nodes and hundreds of edges at most, graphs in LEN are larger. The average size of a graph in LEN has ∼11K nodes and ∼23K edges. We show that state-of-the-art GNN methods give only mediocre results for campaign vs. non-campaign and campaign type classification on LEN. LEN offers a unique and challenging playfield for the graph classification problem. We believe that LEN will help advance the frontiers of graph classification techniques on large networks and also provide an interesting use case in terms of distinguishing coordinated campaigns and organic trends.
17
Dobesova, Zdena. "AUTOMATIC DATA CLASSIFICATION BASED ON THE TRIANGULAR GRAPH FOR THEMATIC MAPS." Geodesy and Cartography 41, no. 1 (2015): 1–8. http://dx.doi.org/10.3846/20296991.2015.1024457.
Повний текст джерелаАнотація:
A triangular point graph helps in the process of data classification for a thematic map. A triangular graph can be used for a situation that is described by three variables. The total sum of variables is 100%. The proportion of three variables is plotted in an equilateral triangular graph where each side represents a coordinate for one variable. A triangular graph displays the proportions of the three variables. The position of the point indicates the type (class) of the situation in the triangular graph. The typology of the situation can be subsequently expressed in the map. We have created a “Triangular Graph” program which represents a helpful automatic tool for ArcGIS software. This new program classifies input data based on a triangular graph. It is realized by two python scripts located in a custom toolbox as two programs. The first program calculates X and Y coordinates in an equilateral triangular graph. The second program compares plotted points and suggested zones of a division produced by the first program. Finally, a new attribute is added to the source data. The user can create a new thematic map, based on that attribute in order to express the typology of the given situation. The programming language Python and essential module ArcPy have been used for solving these tasks. To test the created programs several maps were made, based on the classification often used in demography. For example, the new program helped to create a sample map of age categories in districts of the Czech Republic. The program is available to download from the Esri web pages and web pages of the Department of Geoinformatics, Palacký University Olomouc.
18
Zhou, Yong Zheng, Ru Dong Ouyang, Han Jun Wu, and De Juan Kong. "Research of Ceramic Raw Materials Classification Base on Multivariate Chart Method." Applied Mechanics and Materials 246-247 (December 2012): 1066–70. http://dx.doi.org/10.4028/www.scientific.net/amm.246-247.1066.
Повний текст джерелаАнотація:
In this paper, it was discussed the Rader chart, trigonometric polynomial graph and the constellation graph which can be programmed and realized by MATLAB SOFEWARE and got it applied in the research of Ceramic Raw Materials classification used the replacing theory of same type kaolin to reduce the cost of raw materials and reduce the cost of transport. To improve the economic benefits of the production of ceramic enterprises, and the research results will be widely applied in industrial production for the purpose of guiding ceramic.
19
Cho, Ilwoo, and Palle Jorgensen. "An Index for Graphs and Graph Groupoids." Axioms 11, no. 2 (2022): 47. http://dx.doi.org/10.3390/axioms11020047.
Повний текст джерелаАнотація:
In this paper, we consider certain quantities that arise in the images of the so-called graph-tree indexes of graph groupoids. In text, the graph groupoids are induced by connected finite-directed graphs with more than one vertex. If a graph groupoid GG contains at least one loop-reduced finite path, then the order of G is infinity; hence, the canonical groupoid index G:K of the inclusion K⊆G is either ∞ or 1 (under the definition and a natural axiomatization) for the graph groupoids K of all “parts” K of G. A loop-reduced finite path generates a semicircular element in graph groupoid algebra. Thus, the existence of semicircular systems acting on the free-probabilistic structure of a given graph G is guaranteed by the existence of loop-reduced finite paths in G. The non-semicircularity induced by graphs yields a new index-like notion called the graph-tree index Γ of G. We study the connections between our graph-tree index and non-semicircular cases. Hence, non-semicircularity also yields the classification of our graphs in terms of a certain type of trees. As an application, we construct towers of graph-groupoid-inclusions which preserve the graph-tree index. We further show that such classification applies to monoidal operads.
20
Zhao, Wentao, Dalin Zhou, Xinguo Qiu, and Wei Jiang. "How to Represent Paintings: A Painting Classification Using Artistic Comments." Sensors 21, no. 6 (2021): 1940. http://dx.doi.org/10.3390/s21061940.
Повний текст джерелаАнотація:
The goal of large-scale automatic paintings analysis is to classify and retrieve images using machine learning techniques. The traditional methods use computer vision techniques on paintings to enable computers to represent the art content. In this work, we propose using a graph convolutional network and artistic comments rather than the painting color to classify type, school, timeframe and author of the paintings by implementing natural language processing (NLP) techniques. First, we build a single artistic comment graph based on co-occurrence relations and document word relations and then train an art graph convolutional network (ArtGCN) on the entire corpus. The nodes, which include the words and documents in the topological graph are initialized using a one-hot representation; then, the embeddings are learned jointly for both words and documents, supervised by the known-class training labels of the paintings. Through extensive experiments on different classification tasks using different input sources, we demonstrate that the proposed methods achieve state-of-art performance. In addition, ArtGCN can learn word and painting embeddings, and we find that they have a major role in describing the labels and retrieval paintings, respectively.
21
R. Pappathi, M. P. Syed Ali Nisaya. "l-HILBERT MEAN LABELING OF SOME PATH RELATED GRAPHS." Tuijin Jishu/Journal of Propulsion Technology 44, no. 3 (2023): 4710–16. http://dx.doi.org/10.52783/tjjpt.v44.i3.2637.
Повний текст джерелаАнотація:
Let be a graph with vertices and edges. The th hilbert number is denoted by and is defined by where A - hilbert mean labeling is an injective function , where that induces a bijection defined by
 for all . A graph which admits such labeling is called a - hilbert mean graph. In this paper, a new type of labeling called - hilbert mean labeling is introduced and the path related graphs is studied.
 AMS Subject Classification – 05C78
22
Peng, Feifei, Wei Lu, Wenxia Tan, Kunlun Qi, Xiaokang Zhang, and Quansheng Zhu. "Multi-Output Network Combining GNN and CNN for Remote Sensing Scene Classification." Remote Sensing 14, no. 6 (2022): 1478. http://dx.doi.org/10.3390/rs14061478.
Повний текст джерелаАнотація:
Scene classification is an active research area in the remote sensing (RS) domain. Some categories of RS scenes, such as medium residential and dense residential scenes, would contain the same type of geographical objects but have various spatial distributions among these objects. The adjacency and disjointness relationships among geographical objects are normally neglected by existing RS scene classification methods using convolutional neural networks (CNNs). In this study, a multi-output network (MopNet) combining a graph neural network (GNN) and a CNN is proposed for RS scene classification with a joint loss. In a candidate RS image for scene classification, superpixel regions are constructed through image segmentation and are represented as graph nodes, while graph edges between nodes are created according to the spatial adjacency among corresponding superpixel regions. A training strategy of a jointly learning CNN and GNN is adopted in the MopNet. Through the message propagation mechanism of MopNet, spatial and topological relationships imbedded in the edges of graphs are employed. The parameters of the CNN and GNN in MopNet are updated simultaneously with the guidance of a joint loss via the backpropagation mechanism. Experimental results on the OPTIMAL-31 and aerial image dataset (AID) datasets show that the proposed MopNet combining a graph convolutional network (GCN) or graph attention network (GAT) and ResNet50 achieves state-of-the-art accuracy. The overall accuracy obtained on OPTIMAL-31 is 96.06% and those on AID are 95.53% and 97.11% under training ratios of 20% and 50%, respectively. Spatial and topological relationships imbedded in RS images are helpful for improving the performance of scene classification.
23
Ren, Kai, and Khaliun Buyandelger. "Construction of a Type Knowledge Graph Based on the Value Cognitive Turn of Characteristic Villages: An Application in Jixi, Anhui Province, China." Land 13, no. 1 (2023): 9. http://dx.doi.org/10.3390/land13010009.
Повний текст джерелаАнотація:
Currently, Chinese villages are grappling with the issue of regional value collapse within the long-standing ‘urban-rural dual system’ strategy. Characteristic villages, as integral components of the urban–rural hierarchical spatial system and pivotal agents in rural development, wield significant influence in addressing China’s rural crises. The construction practice of characteristic villages showcases the cognitive evolution of ‘element-industry-function-type’. Within the value perception of characteristic villages, these practices reflect fundamental orientations in the interaction between humans and land, emphasizing the symbiotic relationship between production, life, and ecology. In alignment with this value perception, and drawing upon the existing studies on the classification of characteristic village types in Jixi County, this paper establishes a comprehensive type knowledge graph of characteristic villages. The framework of this graph’s expression revolves around ‘spatial elements-spatial combination-spatial organization’. This graph delineates a knowledge progression encompassing ‘information-knowledge-strategy’, characterized by three levels: the factual knowledge graph, conceptual knowledge graph and regular knowledge graph. The type knowledge graph systematically accumulates insights derived from the spatiotemporal transmission path of the village spatial structure. It formulates a structured progression of knowledge as follows: cognition of the village entity information → analysis of the village landscape structure → examination of the village social relationships. This constructed graph translates type-data information into spatial strategy knowledge, serving as a pivotal process in amalgamating characteristic village spatial data with semantic networks, particularly in expressing authenticity inspection and gene transfer.
24
Melo, Andre, Johanna Völker, and Heiko Paulheim. "Type Prediction in Noisy RDF Knowledge Bases Using Hierarchical Multilabel Classification with Graph and Latent Features." International Journal on Artificial Intelligence Tools 26, no. 02 (2017): 1760011. http://dx.doi.org/10.1142/s0218213017600119.
Повний текст джерелаАнотація:
Semantic Web knowledge bases, in particular large cross-domain data, are often noisy, incorrect, and incomplete with respect to type information. This incompleteness can be reduced, as previous work shows, with automatic type prediction methods. Most knowledge bases contain an ontology defining a type hierarchy, and, in general, entities are allowed to have multiple types (classes of an instance assigned with the rdf:type relation). In this paper, we exploit these characteristics and formulate the type prediction problem as hierarchical multi classification, where the labels are types. We evaluate different sets of features, including entity embeddings, which can be extracted from the knowledge graph exclusively. We propose SLCN, a modification of the local classifier per node approach, which performs feature selection, instance sampling, and class balancing for each local classifier with the objective of improving scalability. Furthermore, we explore different variants of creating features for the classifier, including both graph and latent features. We compare the performance of our proposed method with the state-of-the-art type prediction approach and popular hierarchical multilabel classifiers, and report on experiments with large-scale cross-domain RDF datasets.
25
WILLIAMS, GERALD. "GROUPS OF FIBONACCI TYPE REVISITED." International Journal of Algebra and Computation 22, no. 08 (2012): 1240002. http://dx.doi.org/10.1142/s0218196712400024.
Повний текст джерелаАнотація:
This article concerns a class of groups of Fibonacci type introduced by Johnson and Mawdesley that includes Conway's Fibonacci groups, the Sieradski groups, and the Gilbert–Howie groups. This class of groups provides an interesting focus for developing the theory of cyclically presented groups and, following questions by Bardakov and Vesnin and by Cavicchioli, Hegenbarth, and Repovš, they have enjoyed renewed interest in recent years. We survey results concerning their algebraic properties, such as isomorphisms within the class, the classification of the finite groups, small cancellation properties, abelianizations, asphericity, connections with Labeled Oriented Graph groups, and the semigroups of Fibonacci type. Further, we present a new method of proving the classification of the finite groups that deals with all but three groups.
26
Mallik, Saurav, and Zhongming Zhao. "Graph- and rule-based learning algorithms: a comprehensive review of their applications for cancer type classification and prognosis using genomic data." Briefings in Bioinformatics 21, no. 2 (2019): 368–94. http://dx.doi.org/10.1093/bib/bby120.
Повний текст джерелаАнотація:
AbstractCancer is well recognized as a complex disease with dysregulated molecular networks or modules. Graph- and rule-based analytics have been applied extensively for cancer classification as well as prognosis using large genomic and other data over the past decade. This article provides a comprehensive review of various graph- and rule-based machine learning algorithms that have been applied to numerous genomics data to determine the cancer-specific gene modules, identify gene signature-based classifiers and carry out other related objectives of potential therapeutic value. This review focuses mainly on the methodological design and features of these algorithms to facilitate the application of these graph- and rule-based analytical approaches for cancer classification and prognosis. Based on the type of data integration, we divided all the algorithms into three categories: model-based integration, pre-processing integration and post-processing integration. Each category is further divided into four sub-categories (supervised, unsupervised, semi-supervised and survival-driven learning analyses) based on learning style. Therefore, a total of 11 categories of methods are summarized with their inputs, objectives and description, advantages and potential limitations. Next, we briefly demonstrate well-known and most recently developed algorithms for each sub-category along with salient information, such as data profiles, statistical or feature selection methods and outputs. Finally, we summarize the appropriate use and efficiency of all categories of graph- and rule mining-based learning methods when input data and specific objective are given. This review aims to help readers to select and use the appropriate algorithms for cancer classification and prognosis study.
27
G, Jayagopi, and Pushpa S. "Arrhythmia Classification Based on Combined Chaotic and Statistical Feature Extraction." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 1 (2018): 127. http://dx.doi.org/10.11591/ijeecs.v12.i1.pp127-136.
Повний текст джерелаАнотація:
Obvious information content in Electro cardio graph has become mandatory to reveal the abnormalities in the heart functions. Arrhythmia is commonly seen heart disorder and results in fatal end, if not identified and treated properly within time limits. The straight forward scene in such diagnosis is to detect the salient features from the Electro cardio graph data using signal processing methods followed by proper classification methods. 16 classes of Arrhythmia had been classified in this work by adopting the traditional method of abnormality detection while introducing a novelty in the type of features to be extracted. Lyapunov Exponents, Kolmogorov Sinai Entropy Density, Kolmogorov Sinai Entropy Universality and R-R interval features based on Kurtosis and Skewness had been used to classify the heart beats from the benchmark MIT-Arrhythmia database. Since alternative features had been utilized, common Support Vector Machines based classification could produce an accuracy of 98.95% in the proposed work with just 13 features.
28
G., Jayagopi, and Pushpa S. "Arrhythmia Classification Based on Combined Chaotic and Statistical Feature Extraction." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 1 (2018): 127–36. https://doi.org/10.11591/ijeecs.v12.i1.pp127-136.
Повний текст джерелаАнотація:
Obvious information content in Electro cardio graph has become mandatory to reveal the abnormalities in the heart functions. Arrhythmia is commonly seen heart disorder and results in fatal end, if not identified and treated properly within time limits. The straight forward scene in such diagnosis is to detect the salient features from the Electro cardio graph data using signal processing methods followed by proper classification methods. 16 classes of Arrhythmia had been classified in this work by adopting the traditional method of abnormality detection while introducing a novelty in the type of features to be extracted. Lyapunov Exponents, Kolmogorov Sinai Entropy Density, Kolmogorov Sinai Entropy Universality and R-R interval features based on Kurtosis and Skewness had been used to classify the heart beats from the benchmark MIT-Arrhythmia database. Since alternative features had been utilized, common Support Vector Machines based classification could produce an accuracy of 98.95% in the proposed work with just 13 features.
29
Romanov, Vitaly Anatolyevich, and Vladimir Vladimirovich Ivanov. "Comparison of Graph Embeddings for Source Code with Text Models Based on CNN and CodeBERT Architectures." Proceedings of the Institute for System Programming of the RAS 35, no. 1 (2023): 237–64. http://dx.doi.org/10.15514/ispras-2023-35(1)-15.
Повний текст джерелаАнотація:
One possible way to reduce bugs in source code is to create intelligent tools that make the development process easier. Such tools often use vector representations of the source code and machine learning techniques borrowed from the field of natural language processing. However, such approaches do not take into account the specifics of the source code and its structure. This work studies methods for pretraining graph vector representations for source code, where the graph represents the structure of the program. The results show that graph embeddings allow to achieve an accuracy of classifying variable types in Python programs that is comparable to CodeBERT embeddings. Moreover, the simultaneous use of text and graph embeddings as part of a hybrid model can improve the accuracy of type classification by more than 10%.
30
Zhang, Min, Guohua Geng, Sheng Zeng, and Huaping Jia. "Knowledge Graph Completion for the Chinese Text of Cultural Relics Based on Bidirectional Encoder Representations from Transformers with Entity-Type Information." Entropy 22, no. 10 (2020): 1168. http://dx.doi.org/10.3390/e22101168.
Повний текст джерелаАнотація:
Knowledge graph completion can make knowledge graphs more complete, which is a meaningful research topic. However, the existing methods do not make full use of entity semantic information. Another challenge is that a deep model requires large-scale manually labelled data, which greatly increases manual labour. In order to alleviate the scarcity of labelled data in the field of cultural relics and capture the rich semantic information of entities, this paper proposes a model based on the Bidirectional Encoder Representations from Transformers (BERT) with entity-type information for the knowledge graph completion of the Chinese texts of cultural relics. In this work, the knowledge graph completion task is treated as a classification task, while the entities, relations and entity-type information are integrated as a textual sequence, and the Chinese characters are used as a token unit in which input representation is constructed by summing token, segment and position embeddings. A small number of labelled data are used to pre-train the model, and then, a large number of unlabelled data are used to fine-tune the pre-training model. The experiment results show that the BERT-KGC model with entity-type information can enrich the semantics information of the entities to reduce the degree of ambiguity of the entities and relations to some degree and achieve more effective performance than the baselines in triple classification, link prediction and relation prediction tasks using 35% of the labelled data of cultural relics.
31
Huang, Danyi, Zhuang Bian, Qinli Qiu, Yinmao Wang, Dongmei Fan, and Xiaochang Wang. "Identification of Similar Chinese Congou Black Teas Using an Electronic Tongue Combined with Pattern Recognition." Molecules 24, no. 24 (2019): 4549. http://dx.doi.org/10.3390/molecules24244549.
Повний текст джерелаАнотація:
It is very difficult for humans to distinguish between two kinds of black tea obtained with similar processing technology. In this paper, an electronic tongue was used to discriminate samples of seven different grades of two types of Chinese Congou black tea. The type of black tea was identified by principal component analysis and discriminant analysis. The latter showed better results. The samples of the two types of black tea distributed on the two sides of the region graph were obtained from discriminant analysis, according to tea type. For grade discrimination, we determined grade prediction models for each tea type by partial least-squares analysis; the coefficients of determination of the prediction models were both above 0.95. Discriminant analysis separated each sample in region graph depending on its grade and displayed a classification accuracy of 98.20% by cross-validation. The back-propagation neural network showed that the grade prediction accuracy for all samples was 95.00%. Discriminant analysis could successfully distinguish tea types and grades. As a complement, the models of the biochemical components of tea and electronic tongue by support vector machine showed good prediction results. Therefore, the electronic tongue is a useful tool for Congou black tea classification.
32
POPA, SORIN. "CLASSIFICATION OF ACTIONS OF DISCRETE AMENABLE GROUPS ON AMENABLE SUBFACTORS OF TYPE II." International Journal of Mathematics 21, no. 12 (2010): 1663–95. http://dx.doi.org/10.1142/s0129167x10006343.
Повний текст джерелаАнотація:
We prove a classification result for properly outer actions σ of discrete amenable groups G on strongly amenable subfactors of type II, N ⊂ M, a class of subfactors that were shown to be completely classified by their standard invariant [Formula: see text], in [27]. The result shows that the action σ is completely classified in terms of the action it induces on [Formula: see text]. As an application of this, we obtain that inclusions of type III λ factors, 0 < λ < 1, having discrete decomposition and strongly amenable graph, are completely classified by their standard invariant.
33
Маторин, С. И., С. В. Гуль, and Н. В. Щербинина. "THREE-DIMENSIONAL SYSTEM-OBJECT CLASSIFICATION FOR PREDICTION AND CONTROL SUPPORT." Научно-техническая информация. Серия 2: Информационные процессы и системы, no. 12 (December 1, 2023): 1–13. http://dx.doi.org/10.36535/0548-0027-2023-12-1.
Повний текст джерелаАнотація:
Рассматриваются недостатки современных способов классифицирования объектов и процессов. Предлагается способ построения трехмерных классификаций, позволяющий устранить некоторые из этих недостатков, основанный на системно-объектном подходе, использующем идеи многомерного классифицирования и естественной классификации. В качестве плоскостей классифицирования используются три основные системные характеристики: структурная (узел), функциональная (функция) и субстанциальная (объект), что позволяет осуществлять классифицирование по видам функционального запроса к системе со стороны системы более высокого порядка (надсистемы), по видам процессов становления системы и по полученным результатам. Каждая классификация представляет собой граф типа дерево с одной вершиной, общей для всех трех плоскостей. Представлено формальное описание трехмерного графа средствами дескрипционной логики, что позволяет не только распределять явления и объекты предметной области по классам, но и прослеживать имеющиеся в данной области причинно-следственные связи. Описаны процедуры использования трехмерной системно-объектной классификации для прогнозирования и поддержки управления. Приведен пример трехмерного классифицирования для приборов функциональной диагностики. The shortcomings of modern methods of classification are considered. A method for constructing three-dimensional classifications is proposed, which makes it possible to eliminate some of the shortcomings. The proposed method is based on a system-object approach using the ideas of multidimensional classification and natural classification. Three main system characteristics are used as classification planes: structural (“unit”), functional (“function”) and substantial (“object”), which allows classification by types of functional request to the system, by types of system formation processes and by obtained results. results. Each classification is a tree-type graph with one vertex that is common to all three planes. A formal description of a three-dimensional graph by means of description logic is presented. This method of classification allows not only to distribute the phenomena and objects of the subject area by classes, but also to trace the causal relationships existing in this area. Procedures for using three-dimensional system-object classification for prediction and management support are described. An example of three-dimensional classification for functional diagnostic devices is given.
34
Fionda, Valeria, and Giuseppe Pirrò. "Learning Triple Embeddings from Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3874–81. http://dx.doi.org/10.1609/aaai.v34i04.5800.
Повний текст джерелаАнотація:
Graph embedding techniques allow to learn high-quality feature vectors from graph structures and are useful in a variety of tasks, from node classification to clustering. Existing approaches have only focused on learning feature vectors for the nodes and predicates in a knowledge graph. To the best of our knowledge, none of them has tackled the problem of directly learning triple embeddings. The approaches that are closer to this task have focused on homogeneous graphs involving only one type of edge and obtain edge embeddings by applying some operation (e.g., average) on the embeddings of the endpoint nodes. The goal of this paper is to introduce Triple2Vec, a new technique to directly embed knowledge graph triples. We leverage the idea of line graph of a graph and extend it to the context of knowledge graphs. We introduce an edge weighting mechanism for the line graph based on semantic proximity. Embeddings are finally generated by adopting the SkipGram model, where sentences are replaced with graph walks. We evaluate our approach on different real-world knowledge graphs and compared it with related work. We also show an application of triple embeddings in the context of user-item recommendations.
35
Peng, Rongrong, Changfen Gong, and Shuai Zhao. "Multi-Sensor Information Fusion with Multi-Scale Adaptive Graph Convolutional Networks for Abnormal Vibration Diagnosis of Rolling Mill." Machines 13, no. 1 (2025): 30. https://doi.org/10.3390/machines13010030.
Повний текст джерелаАнотація:
Graph data and multi-sensor information fusion have been integrated into the abnormal vibration type classification and the identification of the rolling mill for extracting spatial–temporal and robust features. However, most of the existing deep learning (DL) based methods exploit only single sensor information and Euclidean space data, which results in incomplete information contained in the features extracted by in-depth networks. To solve this issue, a multi-sensor information fusion with multi-scale adaptive graph convolutional networks (M2AGCNs) framework is proposed to model graph data and multi-sensor information fusion in a unified in-depth network and then to achieve abnormal vibration diagnosis. First, convolutional neural networks (CNNs) were adopted for the deeper features of multi-sensor signals. And then, the extracted features were fed into the proposed feature-driven adaptive graph generation network to build graphs to extract spatial–temporal correlation between multi-sensor data. After that, the multi-scale graph convolutional networks (MSGCNs) were employed to aggregate and enrich several different receptive information to further improve valuable features. Finally, the extracted multi-sensor features were integrated into a unified network to achieve the abnormal vibration type classification and identification of the rolling mill. Meanwhile, we performed horizontal, vertical, and coupled abnormal vibration experiments, and then three different types of studies were conducted to illustrate the superiority and usefulness of this method in the paper and the feasibility of rolling mill abnormal vibration diagnosis. It can be seen from the results that the proposed M2AGCNs can be able to achieve valuable feature extraction effectively from multi-sensor information and to obtain more excellent behavior of the abnormal vibration diagnosis of the rolling mill in comparison with the mainstream methods.
36
Yuzana, Win, and Masada Tomonari. "Bidirectional Extraction of Phrases for Expanding Queries in Academic Paper Retrieval." International Journal of Advanced Research in Artificial Intelligence (IJARAI) Volume 5, Issue 1 (2016): 27–33. https://doi.org/10.14569/IJARAI.2016.050105.
Повний текст джерелаАнотація:
This paper proposes a new method for query expansion based on bidirectional extraction of phrases as word n-grams from research paper titles. The proposed method aims to extract information relevant to users’ needs and interests and thus to provide a useful system for technical paper retrieval. The outcome of proposed method are the trigrams as phrases that can be used for query expansion. <em>First</em>, word trigrams are extracted from research paper titles. <em>Second</em>, a co-occurrence graph of the extracted trigrams is constructed. To construct the co-occurrence graph, the direction of edges is considered in two ways: <em>forward</em> and <em>reverse</em>. In the forward and reverse co-occurrence graphs, the trigrams point to other trigrams appearing after and before them in a paper title,respectively.<em> Third</em>, Jaccard similarity is computed between trigrams as the weight of the graph edge. <em>Fourth</em>, the weighted version of PageRank is applied. Consequently, the following two types of phrases can be obtained as the trigrams associated with the higher PageRank scores. The trigrams of the one type, which are obtained from the forward co-occurrence graph, can form a more specific query when users add a technical word or words before them. Those of the other type, obtained from the reverse co-occurrence graph, can form a more specific query when users add a technical word or words after them. The extraction of phrases is evaluated as additional features in the paper title classification task using SVM. The experimental results show that the classification accuracy is improved than the accuracy achieved when the standard TF-IDF text features are only used. Moreover, the trigrams extracted by the proposed method can be utilized to expand query words in research paper retrieval.
37
Wang, Yan, Xiao Feng Ma, and Miao Zhu. "A knowledge graph algorithm enabled deep recommendation system." PeerJ Computer Science 10 (July 30, 2024): e2010. http://dx.doi.org/10.7717/peerj-cs.2010.
Повний текст джерелаАнотація:
Personalized learning resource recommendations may help resolve the difficulties of online education that include learning mazes and information overload. However, existing personalized learning resource recommendation algorithms have shortcomings such as low accuracy and low efficiency. This study proposes a deep recommendation system algorithm based on a knowledge graph (D-KGR) that includes four data processing units. These units are the recommendation unit (RS unit), the knowledge graph feature representation unit (KGE unit), the cross compression unit (CC unit), and the feature extraction unit (FE unit). This model integrates technologies including the knowledge graph, deep learning, neural network, and data mining. It introduces cross compression in the feature learning process of the knowledge graph and predicts user attributes. Multimodal technology is used to optimize the process of project attribute processing; text type attributes, multivalued type attributes, and other type attributes are processed separately to reconstruct the knowledge graph. A convolutional neural network algorithm is introduced in the reconstruction process to optimize the data feature qualities. Experimental analysis was conducted from two aspects of algorithm efficiency and accuracy, and the particle swarm optimization, neural network, and knowledge graph algorithms were compared. Several tests showed that the deep recommendation system algorithm had obvious advantages when the number of learning resources and users exceeded 1,000. It has the ability to integrate systems such as the particle swarm optimization iterative classification, neural network intelligent simulation, and low resource consumption. It can quickly process massive amounts of information data, reduce algorithm complexity and requires less time and had lower costs. Our algorithm also has better efficiency and accuracy.
38
Meng, Xiangzhen, Bo Jing, Shenglong Wang, Jinxin Pan, Yifeng Huang, and Xiaoxuan Jiao. "Fault Knowledge Graph Construction and Platform Development for Aircraft PHM." Sensors 24, no. 1 (2023): 231. http://dx.doi.org/10.3390/s24010231.
Повний текст джерелаАнотація:
To tackle the problems of over-reliance on traditional experience, poor troubleshooting robustness, and slow response by maintenance personnel to changes in faults in the current aircraft health management field, this paper proposes the use of a knowledge graph. The knowledge graph represents troubleshooting in a new way. The aim of the knowledge graph is to improve the correlation between fault data by representing experience. The data source for this study consists of the flight control system manual and typical fault cases of a specific aircraft type. A knowledge graph construction approach is proposed to construct a fault knowledge graph for aircraft health management. Firstly, the data are classified using the ERNIE model-based method. Then, a joint entity relationship extraction model based on ERNIE-BiLSTM-CRF-TreeBiLSTM is introduced to improve entity relationship extraction accuracy and reduce the semantic complexity of the text from a linguistic perspective. Additionally, a knowledge graph platform for aircraft health management is developed. The platform includes modules for text classification, knowledge extraction, knowledge auditing, a Q&A system, and graph visualization. These modules improve the management of aircraft health data and provide a foundation for rapid knowledge graph construction and knowledge graph-based fault diagnosis.
39
Ma, Xuesong, and Ruji Wang. "Trivalent Non-symmetric Vertex-Transitive Graphs of Order at Most 150." Algebra Colloquium 15, no. 03 (2008): 379–90. http://dx.doi.org/10.1142/s1005386708000370.
Повний текст джерелаАнотація:
Let X be a simple undirected connected trivalent graph. Then X is said to be a trivalent non-symmetric graph of type (II) if its automorphism group A = Aut (X) acts transitively on the vertices and the vertex-stabilizer Av of any vertex v has two orbits on the neighborhood of v. In this paper, such graphs of order at most 150 with the basic cycles of prime length are investigated, and a classification is given for such graphs which are non-Cayley graphs, whose block graphs induced by the basic cycles are non-bipartite graphs.
40
Zhong, Zhicheng, Jixiang Wan, Hao Bu, et al. "Individual Importance Classification of Urban Stormwater Channel Networks: A Novel Approach Based on Permutation and Algebraic Graph Theory." Water 16, no. 22 (2024): 3242. http://dx.doi.org/10.3390/w16223242.
Повний текст джерелаАнотація:
The frequency and intensity of urban flooding continuously increase due to the dual influences of climate change and urbanization. Conducting individual importance classification of urban stormwater channel networks (USCNs) is of significant importance for alleviating urban flooding and facilitating targeted stormwater management implementation. However, a quantitative classification method is lacking for trellis networks, which are a common type of USCN. This study proposed a novel importance classification methodology for channel segments in most types of USCNs, especially suitable for trellis networks, based on permutation and algebraic graph theory. The concept of permutation was integrated into the methodology to measure the importance of each channel segment to the USCN. Algebraic graph theory was employed to quantify the topological structure and hydraulic characteristics of the USCN. To verify the applicability and rationality of the proposed methodology, a real-world city with trellis USCNs in China (i.e., Huai’an) was selected as the study area. Seventy channel segments in the USCN were efficiently classified into three categories based on individual importance. This study provided a decision-support methodology from the perspective of individual importance classification in the USCN and offered valuable reference for urban flooding managers.
41
Maheswara Rao, Saka Uma, Keshetti Sreekala, Pulluri Srinivas Rao, Nalla Shirisha, Gunnam Srinivas, and Erry Sreedevi. "Plant disease classification using novel integration of deep learning CNN and graph convolutional networks." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 3 (2024): 1721. http://dx.doi.org/10.11591/ijeecs.v36.i3.pp1721-1730.
Повний текст джерелаАнотація:
Plant diseases present substantial challenges to global agriculture, significantly affecting crop yields and jeopardizing food security. Accurate and timely detection of these diseases is paramount for mitigating their adverse effects. This paper proposes a novel approach for plant disease classification by integrating convolutional neural networks (CNNs) and graph convolutional networks (GCNs). The model aims to enhance classification accuracy by leveraging both visual features extracted by CNNs and relational information captured by GCNs. Using a Kaggle dataset containing images of diseased and healthy plant leaves from 31 classes, including apple, corn, grape, peach, pepper bell, potato, strawberry, and tomato. Standalone CNN models were trained on image data from each plant type, while standalone GCN models utilized graph-structured data representing plant relationships within each subset. The proposed integrated CNN-GCN model capitalizes on the complementary strengths of CNNs and GCNs to achieve improved classification performance. Through rigorous experimentation and comparative analysis, the effectiveness of the integrated CNN-GCN approach was evaluated alongside standalone CNN and GCN models across all plant types. Results demonstrated the superiority of the integrated model, highlighting its potential for enhancing plant disease classification accuracy.
42
Saka, Uma Maheswara Rao Keshetti Sreekala Pulluri Srinivas Rao Nalla Shirisha Gunnam Srinivas Erry Sreedevi. "Plant disease classification using novel integration of deep learning CNN and graph convolutional networks." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 3 (2024): 1721–30. https://doi.org/10.11591/ijeecs.v36.i3.pp1721-1730.
Повний текст джерелаАнотація:
Plant diseases present substantial challenges to global agriculture, significantly affecting crop yields and jeopardizing food security. Accurate and timely detection of these diseases is paramount for mitigating their adverse effects. This paper proposes a novel approach for plant disease classification by integrating convolutional neural networks (CNNs) and graph convolutional networks (GCNs). The model aims to enhance classification accuracy by leveraging both visual features extracted by CNNs and relational information captured by GCNs. Using a Kaggle dataset containing images of diseased and healthy plant leaves from 31 classes, including apple, corn, grape, peach, pepper bell, potato, strawberry, and tomato. Standalone CNN models were trained on image data from each plant type, while standalone GCN models utilized graph-structured data representing plant relationships within each subset. The proposed integrated CNN-GCN model capitalizes on the complementary strengths of CNNs and GCNs to achieve improved classification performance. Through rigorous experimentation and comparative analysis, the effectiveness of the integrated CNN-GCN approach was evaluated alongside standalone CNN and GCN models across all plant types. Results demonstrated the superiority of the integrated model, highlighting its potential for enhancing plant disease classification accuracy.
43
Radha, K., and S. Sri Harini. "Characterizations of the Direct Sum of Two Difference - Mean Fuzzy Graphs." Indian Journal Of Science And Technology 17, no. 20 (2024): 2043–49. http://dx.doi.org/10.17485/ijst/v17i20.1017.
Повний текст джерелаАнотація:
Objectives: This study presents a new type of fuzzy graph known as the difference mean fuzzy graph by introducing difference mean edge. Methodology: In this paper, difference mean edge in a fuzzy graph is defined by considering the relationship between the membership value of the edge and the membership values of its end vertices. Also, difference mean fuzzy graph is defined and its properties are derived. Findings: The difference mean edge and the difference mean fuzzy graph are introduced. The requirements for an edge in the direct sum of two fuzzy graphs to be a difference mean edge are found in this study. Additionally, conditions are derived such that the direct sum of two fuzzy graphs is a difference mean fuzzy graph. Novelty: Depending on the membership values of the edges and vertices, effective edge in fuzzy graph have already been defined. A new concept of difference mean edge in fuzzy graph is introduced. Using this, difference mean fuzzy graph is also introduced. Characterizations of the difference mean edge in the direct sum of fuzzy graphs are attained. The requirements for the necessary and sufficient component of difference mean fuzzy graphs to be a direct sum are suggested. Mathematics Subject Classification (2020): 05C72, 05C76. Keywords: Difference mean edge, Difference Mean fuzzy graph, Effective fuzzy graph, Effective difference mean edge, Direct sum
44
LIU, MINGXIA, and DAOQIANG ZHANG. "SPARSITY SCORE: A NOVEL GRAPH-PRESERVING FEATURE SELECTION METHOD." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 04 (2014): 1450009. http://dx.doi.org/10.1142/s0218001414500098.
Повний текст джерелаАнотація:
As thousands of features are available in many pattern recognition and machine learning applications, feature selection remains an important task to find the most compact representation of the original data. In the literature, although a number of feature selection methods have been developed, most of them focus on optimizing specific objective functions. In this paper, we first propose a general graph-preserving feature selection framework where graphs to be preserved vary in specific definitions, and show that a number of existing filter-type feature selection algorithms can be unified within this framework. Then, based on the proposed framework, a new filter-type feature selection method called sparsity score (SS) is proposed. This method aims to preserve the structure of a pre-defined l1 graph that is proven robust to data noise. Here, the modified sparse representation based on an l1-norm minimization problem is used to determine the graph adjacency structure and corresponding affinity weight matrix simultaneously. Furthermore, a variant of SS called supervised SS (SuSS) is also proposed, where the l1 graph to be preserved is constructed by using only data points from the same class. Experimental results of clustering and classification tasks on a series of benchmark data sets show that the proposed methods can achieve better performance than conventional filter-type feature selection methods.
45
Wu, Yang-Han, Yu-An Huang, Jian-Qiang Li, et al. "Knowledge graph embedding for profiling the interaction between transcription factors and their target genes." PLOS Computational Biology 19, no. 6 (2023): e1011207. http://dx.doi.org/10.1371/journal.pcbi.1011207.
Повний текст джерелаАнотація:
Interactions between transcription factor and target gene form the main part of gene regulation network in human, which are still complicating factors in biological research. Specifically, for nearly half of those interactions recorded in established database, their interaction types are yet to be confirmed. Although several computational methods exist to predict gene interactions and their type, there is still no method available to predict them solely based on topology information. To this end, we proposed here a graph-based prediction model called KGE-TGI and trained in a multi-task learning manner on a knowledge graph that we specially constructed for this problem. The KGE-TGI model relies on topology information rather than being driven by gene expression data. In this paper, we formulate the task of predicting interaction types of transcript factor and target genes as a multi-label classification problem for link types on a heterogeneous graph, coupled with solving another link prediction problem that is inherently related. We constructed a ground truth dataset as benchmark and evaluated the proposed method on it. As a result of the 5-fold cross experiments, the proposed method achieved average AUC values of 0.9654 and 0.9339 in the tasks of link prediction and link type classification, respectively. In addition, the results of a series of comparison experiments also prove that the introduction of knowledge information significantly benefits to the prediction and that our methodology achieve state-of-the-art performance in this problem.
46
K, Radha, and Sri Harini S. "Characterizations of the Direct Sum of Two Difference - Mean Fuzzy Graphs." Indian Journal of Science and Technology 17, no. 20 (2024): 2043–49. https://doi.org/10.17485/IJST/v17i20.1017.
Повний текст джерелаАнотація:
Abstract <strong>Objectives:</strong> This study presents a new type of fuzzy graph known as the difference mean fuzzy graph by introducing difference mean edge.<strong> Methodology:</strong> In this paper, difference mean edge in a fuzzy graph is defined by considering the relationship between the membership value of the edge and the membership values of its end vertices. Also, difference mean fuzzy graph is defined and its properties are derived.<strong> Findings:</strong> The difference mean edge and the difference mean fuzzy graph are introduced. The requirements for an edge in the direct sum of two fuzzy graphs to be a difference mean edge are found in this study. Additionally, conditions are derived such that the direct sum of two fuzzy graphs is a difference mean fuzzy graph. <strong>Novelty:</strong> Depending on the membership values of the edges and vertices, effective edge in fuzzy graph have already been defined. A new concept of difference mean edge in fuzzy graph is introduced. Using this, difference mean fuzzy graph is also introduced. Characterizations of the difference mean edge in the direct sum of fuzzy graphs are attained. The requirements for the necessary and sufficient component of difference mean fuzzy graphs to be a direct sum are suggested. Mathematics Subject Classification (2020): 05C72, 05C76. <strong>Keywords:</strong> Difference mean edge, Difference Mean fuzzy graph, Effective fuzzy graph, Effective difference mean edge, Direct sum
47
Wang, Ruiheng, Hongliang Zhu, Lu Wang, Zhaoyun Chen, Mingcheng Gao, and Yang Xin. "User Identity Linkage Across Social Networks by Heterogeneous Graph Attention Network Modeling." Applied Sciences 10, no. 16 (2020): 5478. http://dx.doi.org/10.3390/app10165478.
Повний текст джерелаАнотація:
Today, social networks are becoming increasingly popular and indispensable, where users usually have multiple accounts. It is of considerable significance to conduct user identity linkage across social networks. We can comprehensively depict diversified characteristics of user behaviors, accurately model user profiles, conduct recommendations across social networks, and track cross social network user behaviors by user identity linkage. Existing works mainly focus on a specific type of user profile, user-generated content, and structural information. They have problems of weak data expression ability and ignored potential relationships, resulting in unsatisfactory performances of user identity linkage. Recently, graph neural networks have achieved excellent results in graph embedding, graph representation, and graph classification. As a graph has strong relationship expression ability, we propose a user identity linkage method based on a heterogeneous graph attention network mechanism (UIL-HGAN). Firstly, we represent user profiles, user-generated content, structural information, and their features in a heterogeneous graph. Secondly, we use multiple attention layers to aggregate user information. Finally, we use a multi-layer perceptron to predict user identity linkage. We conduct experiments on two real-world datasets: OSCHINA-Gitee and Facebook-Twitter. The results validate the effectiveness and advancement of UIL-HGAN by comparing different feature combinations and methods.
48
Nam, Tran Le. "Classification of j-maximal spacelike affine translation surfaces in the Minkovski space i31 with density." Tạp chí Khoa học 15, no. 3 (2019): 36. http://dx.doi.org/10.54607/hcmue.js.15.3.140(2018).
Повний текст джерелаАнотація:
An affine translation surface is a graph of a function introduced by Liu and Yu in 2013. The article considers the spacelike affine translation surfaces in the Minkowski space with density establishing the Lagrange’s equation type for -maximal surface, classifying -maximal spacelike affine translation surfaces. The result obtains two parameters and . From that, the Calabi – Bernstein theorem in this space is not true because two function and are defined on
49
Babkov, Yu V., E. E. Belova, and M. I. Potapov. "On the classification of motive power failures." Dependability 21, no. 4 (2021): 12–19. http://dx.doi.org/10.21683/1729-2646-2021-21-4-12-19.
Повний текст джерелаАнотація:
The Aim of the article is to develop a motive power failure classification to enable substantiated definition of dependability requirements for motive power as a part of a railway transportation system, as well as for organizing systematic measures to ensure a required level of its dependability over the life cycle. Methods. The terminology of interstate dependability-related standards was analysed and the two classifications used by OJSC “RZD” for estimating the dependability of technical systems and motive power were compared. The dependability of railway transportation systems is studied using structural and logical and logical and probabilistic methods of dependability analysis, while railway lines are examined using the graph theory and the Markov chains. Results. An analysis of the existing failure classifications identified shortcomings that prevent the use of such classifications for studying the structural dependability of such railway transportation systems as motive power. A classification was developed that combines two failure classifications (“category-based” for the transportation process and technical systems and “type-based” for the motive power), but this time with new definitions. The proposed classification of the types of failures involves stricter definitions of the conditions and assumptions required for evaluating the dependability and technical condition of an item, which ensures correlation between the characteristics of motive power and its dependability throughout the life cycle in the context of the above tasks. The two classifications could be used simultaneously while researching structural problems of dependability using logical and probabilistic methods and Markov chains. The developed classification is included in the provisions of the draft interstate standard “Dependability of motive power. Procedure for the definition, calculation methods and supervision of dependability indicators throughout the life cycle” that is being prepared by JSC “VNIKTI” in accordance with the OJSC “RZD” research and development plan. Conclusion. The article’s findings will be useful to experts involved in the evaluation of motive power dependability.
50
Mubeen, Azmath, and Uma N. Dulhare. "Feature Extraction and Identification of Rheumatoid Nodules Using Advanced Image Processing Techniques." Rheumato 4, no. 4 (2024): 176–92. http://dx.doi.org/10.3390/rheumato4040014.
Повний текст джерелаАнотація:
Background/Objectives: Accurate detection and classification of nodules in medical images, particularly rheumatoid nodules, are critical due to the varying nature of these nodules, where their specific type is often unknown before analysis. This study addresses the challenges of multi-class prediction in nodule detection, with a specific focus on rheumatoid nodules, by employing a comprehensive approach to feature extraction and classification. We utilized a diverse dataset of nodules, including rheumatoid nodules sourced from the DermNet dataset and local rheumatologists. Method: This study integrates 62 features, combining traditional image characteristics with advanced graph-based features derived from a superpixel graph constructed through Delaunay triangulation. The key steps include image preprocessing with anisotropic diffusion and Retinex enhancement, superpixel segmentation using SLIC, and graph-based feature extraction. Texture analysis was performed using Gray-Level Co-occurrence Matrix (GLCM) metrics, while shape analysis was conducted with Fourier descriptors. Vascular pattern recognition, crucial for identifying rheumatoid nodules, was enhanced using the Frangi filter. A Hybrid CNN–Transformer model was employed for feature fusion, and feature selection and hyperparameter tuning were optimized using Gray Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). Feature importance was assessed using SHAP values. Results: The proposed methodology achieved an accuracy of 85%, with a precision of 0.85, a recall of 0.89, and an F1 measure of 0.87, demonstrating the effectiveness of the approach in detecting and classifying rheumatoid nodules in both binary and multi-class classification scenarios. Conclusions: This study presents a robust tool for the detection and classification of nodules, particularly rheumatoid nodules, in medical imaging, offering significant potential for improving diagnostic accuracy and aiding in the early identification of rheumatoid conditions.