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Articoli di riviste sul tema "Network model"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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1

HRABCAK, David, and Lubomir DOBOS. "THE CONCEPT OF MULTILAYERED NETWORK MODEL FOR 5G NETWORKS." Acta Electrotechnica et Informatica 19, no. 3 (2019): 39–43. http://dx.doi.org/10.15546/aeei-2019-0022.

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Koide, Satoshi, Hiroshi Ohno, Ryuta Terashima, Thanomsak Ajjanapanya, and Itti Rittaporn. "Hidden Markov Flow Network Model: A Generative Model for Dynamic Flow on a Network." International Journal of Machine Learning and Computing 4, no. 4 (2014): 319–27. http://dx.doi.org/10.7763/ijmlc.2014.v4.431.

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3

Scott, Gary M., and W. Harmon Ray. "Neural Network Process Models Based on Linear Model Structures." Neural Computation 6, no. 4 (1994): 718–38. http://dx.doi.org/10.1162/neco.1994.6.4.718.

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The KBANN (Knowledge-Based Artificial Neural Networks) approach uses neural networks to refine knowledge that can be written in the form of simple propositional rules. This idea is extended by presenting the MANNIDENT (Multivariable Artificial Neural Network Identification) algorithm by which the mathematical equations of linear dynamic process models determine the topology and initial weights of a network, which is further trained using backpropagation. This method is applied to the task of modeling a nonisothermal chemical reactor in which a first-order exothermic reaction is occurring. This

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Hrabcak, David, Lubomir Dobos, Jan Papaj, and Lubos Ovsenik. "Multilayered Network Model for Mobile Network Infrastructure Disruption." Sensors 20, no. 19 (2020): 5491. http://dx.doi.org/10.3390/s20195491.

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In this paper, the novel study of the multilayered network model for the disrupted infrastructure of the 5G mobile network is introduced. The aim of this study is to present the new way of incorporating different types of networks, such as Wireless Sensor Networks (WSN), Mobile Ad-Hoc Networks (MANET), and DRONET Networks into one fully functional multilayered network. The proposed multilayered network model also presents the resilient way to deal with infrastructure disruption due to different reasons, such as disaster scenarios or malicious actions. In the near future, new network technologi

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Usui, Shohei, Fujio Toriumi, Masato Matsuo, Takatsugu Hirayama, and Kenji Mase. "Greedy Network Growth Model of Social Network Service." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 4 (2014): 590–97. http://dx.doi.org/10.20965/jaciii.2014.p0590.

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As new network communication tools are developed, social network services (SNS) such as Facebook and Twitter are becoming part of a social phenomenon globally impacting on society. Many researchers are therefore studying the structure of relationship networks among users. We propose a greedy network growth model that appropriately increases nodes and links while automatically reproducing the target network. We handle a wide range of networks with high expressive ability. Results of experiments showed that we accurately reproduced 92.4% of 189 target networks from real services. The model also

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6

Pardo, Raúl, and Gerardo Schneider. "Model Checking Social Network Models." Electronic Proceedings in Theoretical Computer Science 256 (September 6, 2017): 238–52. http://dx.doi.org/10.4204/eptcs.256.17.

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7

Xu, Shuai, and Bai Da Zhang. "Complex Network Model and its Application." Advanced Materials Research 791-793 (September 2013): 1589–92. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1589.

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Human life is in a complex network world. In everyday life, the network can be a physical object such as the Internet, power network, road network and neural network; can also abstract not touch, such as interpersonal networks, networks of co-operation in scientific research, product supply chain network, biological populations, networks, etc.. The topology of these networks, the statistical characteristics and the formation mechanism, and so on, has a very important significance for the efficient allocation of resources, provides various functions, as well as the stability of the network, how

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8

Murugan, S., and Dr M. Jeyakarthic. "Optimal Deep Neural Network based Classification Model for Intrusion Detection in Mobile Adhoc Networks." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 1374–87. http://dx.doi.org/10.5373/jardcs/v11sp10/20192983.

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Ouassit, Youssef. "CT Liver Segmentation: A Capsules Network Model." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (2020): 1016–24. http://dx.doi.org/10.5373/jardcs/v12sp4/20201574.

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10

Venkatesh, Raghav, and Raja Muthalagu. "Network security prediction model using neural networks." Journal of Physics: Conference Series 1706 (December 2020): 012167. http://dx.doi.org/10.1088/1742-6596/1706/1/012167.

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11

Djellali, Choukri, and Mehdi adda. "An Enhanced Deep Learning Model to Network Attack Detection, by using Parameter Tuning, Hidden Markov Model and Neural Network." Journal of Ubiquitous Systems and Pervasive Networks 15, no. 01 (2021): 35–41. http://dx.doi.org/10.5383/juspn.15.01.005.

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In recent years, Deep Learning has become a critical success factor for Machine Learning. In the present study, we introduced a Deep Learning model to network attack detection, by using Hidden Markov Model and Artificial Neural Networks. We used a model aggregation technique to find a single consolidated Deep Learning model for better data fitting. The model selection technique is applied to optimize the bias-variance trade-off of the expected prediction. We demonstrate its ability to reduce the convergence, reach the optimal solution and obtain more cluttered decision boundaries. Experimental

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12

Xiao, Wen Hong, and Xiang Dong Cai. "A Novel Wireless Sensor Network Model Based on Complex Network Theory." Advanced Materials Research 546-547 (July 2012): 1276–82. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.1276.

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The key issue of wireless sensor networks is to balance the energy costs of the entire network, to enhance the robustness of the entire sensor network. Sensor networks as a special kind of complex network, in particular, environmental constraints, and more from the traditional complex networks, such as Internet networks, ecological networks, social networks, is to introduce a way of wireless sensor networks way of complex networks theory and analytical method, the key lies in, which is a successful model of complex network theory and analysis methods, more suitable for the application of wirel

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13

Yang, Hong Mei, Chun Ying Zhang, Rui Tao Liang, and Fang Tian. "Set Pair Social Network Analysis Model." Applied Mechanics and Materials 50-51 (February 2011): 63–67. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.63.

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Through the study on social network information, this paper explore that there exists the certain and uncertain phenomena in the process of finding the relationship between individuals by using social networks, and the social networks are constantly changing. In light of there are some uncertainty and dynamic problems for the network, this paper put forward the set pair social network analysis model and set pair social network analysis model and its properties.

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14

Vakulenko, S., and M. Zimin. "An Analytically Tractable Model of Large Network." International Journal of Nanotechnology and Molecular Computation 2, no. 1 (2010): 1–12. http://dx.doi.org/10.4018/jnmc.2010010101.

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This paper considers specially organized networks of large size. They can serve as models of computer communication systems, economical systems, neural and genetic networks. The topology of this network is simple and the analysis of the network behaviour is an analytically tractable task, while computer simulations are difficult. The authors show that such networks generate any structurally stable attractors in particular chaotic and periodic. They can simulate all Turing machines, that is, perform any computations. In noisy cases, the reliability of such network is exponentially high as a fun

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15

Jurenoks, Aleksejs, and Leonids Novickis. "Wireless sensor networks lifetime assessment model development." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 3 (June 16, 2015): 121. http://dx.doi.org/10.17770/etr2015vol3.508.

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<p class="R-AbstractKeywords"><span lang="EN-US">In the recent years low power computing systems have gained popularity. Networks, which use low power computer systems and transmitted data by using wireless connection are called wireless sensor networks, which main task is to get the information from sensors and transmission network. Nowadays, the most topical researches pertaining to wireless sensor networks are grounded on the new optimization of structure of network transmission protocol, the routing optimization in transmission network, optimization of network structure, as a r

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SERA, TAKAFUMI, and AKIRA TAKURA. "TASK GENERATION FOR DISTRIBUTED FUNCTIONAL MODEL." International Journal on Artificial Intelligence Tools 05, no. 03 (1996): 277–90. http://dx.doi.org/10.1142/s0218213096000183.

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A new method is proposed to automatically generate the network control specifications necessary for executing services in a network from communications service specifications by using a knowledge of virtual resource that expresses logical functions in a network. Because these communication service specifications are described by observable terminal behaviors, the communication service specifications can be described without detailed knowledge of the network. The conventional stepwise description for network control specifications is manually performed for each network architecture. Then an exp

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Krido, Eko Cahyono, Sudarmiatin, and Bidin Rosmiza. "Internationalization for SMEs and Strategies for success in the Business: A case study SMEs shoes in Mojokerto, Indonesia." International Journal of Social Science and Human Research 05, no. 04 (2022): 1532–39. https://doi.org/10.5281/zenodo.6504899.

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Internationalization of small and medium-sized enterprises is a topic that has been studied in various ways. Different scholars have tackled this topic from different perspectives and have found that there is no single way to internationalization. This study aims to build on previous literature by applying ethnographic studies of Mojokerto SMEs, which have been actively exporting to Asia since its inception. The goal was to find a variety of Theorem connections and applications for small businesses. By, this study challenges SME faced, but process of internationalization is also highlighted. W

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Fahd, Syed Muhammed. "Artificial Neural Network Model for Friction Stir Processing." International Journal of Engineering Research 3, no. 6 (2014): 396–97. http://dx.doi.org/10.17950/ijer/v3s6/606.

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P., Tymoshchuk. "SIMPLIFIED PARALLEL SORTING DISCRETE-TIME NEURAL NETWORK MODEL." Computer systems and network 2, no. 1 (2017): 94–101. http://dx.doi.org/10.23939/csn2020.01.094.

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A model of parallel sorting neural network of discrete-time has been proposed. The model is described by system of difference equations and by step functions. The model is based on simplified neural circuit of discrete-time that identifies maximal/minimal values of input data and is described by difference equation and by step functions. A bound from above on a number of iterations required for reaching convergence of search process to steady state is determined. The model does not need a knowledge of change range of input data. In order to use the model a minimal difference between values of

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20

KUKURUDZ, Rostyslav. "MODEL OF ECONOMIC PARTNERSHIP IN THE NETWORK ECONOMY." Herald of Khmelnytskyi National University. Economic sciences 312, no. 6(2) (2022): 40–44. http://dx.doi.org/10.31891/2307-5740-2022-312-6(2)-7.

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Significant changes in the development of new interdependencies between companies, the development of the value chain concept, and technological progress in the field of electronic communications between companies are the main prerequisites for the significant interest of theorists in network structures. Some scholars believe that the study of two-way relationships reflects the popularity of the concept of affiliate marketing, while the focus on networks is a higher level of marketing development. Given that the achievement of long-term cooperative trade relations primarily depends on the will

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21

Longson, S. Yenigha, O. Ahiakwo Christopher, C. Idoniboyeobu Dikio, and I. Braide Sepribo. "Improved Randomized Flow Model for 330kV Electric Power Transmission Network." European Journal of Advances in Engineering and Technology 9, no. 11 (2022): 1–11. https://doi.org/10.5281/zenodo.10646886.

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<strong>ABSTRACT</strong> The study of improved randomized flow model for 330kV electric power transmission network in Nigeria focused on the Benin and Oshogbo regional 330kV network. This research is based on a randomize model and mathematical reliability assessment of the existing 330kV power transmission networks in Benin and Oshogbo regions. The network centrality values, performance characteristic, probability of failure and reliability were evaluated. With the collected data and networks information a randomized algorithm was develop, coded and implemented in MATLAB R2016a and mathematic

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22

Zijlstra, Bonne J. H., Marijtje A. J. van Duijn, and Tom A. B. Snijders. "The Multilevel p2 Model." Methodology 2, no. 1 (2006): 42–47. http://dx.doi.org/10.1027/1614-2241.2.1.42.

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The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was

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23

Yan, Yu-Wei, Yuan Jiang, Song-Qing Yang, Rong-Bin Yu, and Cheng Hong. "Network failure model based on time series." Acta Physica Sinica 71, no. 8 (2022): 088901. http://dx.doi.org/10.7498/aps.71.20212106.

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With the development of network science, the static network has been unable to clearly characterize the dynamic process of the network. In real networks, the interaction between individuals evolves rapidly over time. This network model closely links time to interaction process. Compared with static networks, dynamic networks can clearly describe the interaction time of nodes, which has more practical significance. Therefore, how to better describe the behavior changes of networks after being attacked based on time series is an important problem in the existing cascade failure research. In orde

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Shi, Haoqiang. "Intelligent network management model based on Bayesian network." Applied and Computational Engineering 6, no. 1 (2023): 813–19. http://dx.doi.org/10.54254/2755-2721/6/20230931.

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Nowadays, with the vigorous promotion of digital applications, major enterprises have established their own enterprise networks, in which there are many network equipment, communication lines, servers and various applications. To ensure the stable running of the network system, the enterprise establishes a special network management system to monitor the network running status. The network management system can detect all kinds of network faults, such as line interruption, port down, and service application exception alarms. However, the usual network management system can only discover and de

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Konovalov, V. A. "MARKOV NETWORK MODEL." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 231 (September 2023): 27–37. http://dx.doi.org/10.14489/vkit.2023.09.pp.027-037.

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Markov networks synthesized from the N-scheme of the Markov algorithm, developed to replace his well-known y-scheme, are studied theoretically from the standpoint of theories of algorithms and categories. The provisions of the theory of Markov algorithms are developed in the direction of synthesizing methods for the joint processing of words and morphism values that are occurrences in these words. In Markov networks, morphisms are weighted according to the method adopted in artificial neural networks. The material for the synthesis of Markov networks are diagrams of Markov occurrences of words

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KASHTALIAN, A., and O. SAVENKO. "SECURITY IMPROVEMENT AND THE MODEL OF ANTI-VIRUS INTELLECTUAL HONEYNET IN CORPORATE COMPUTER NETWORKS." Herald of Khmelnytskyi National University. Technical sciences 287, no. 4 (2020): 33–38. https://doi.org/10.31891/2307-5732-2020-287-4-33-38.

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The article proposes a model and concept of building a network of intelligent honeynet deployed in a computer network. The proposed network is a multilevel system that includes many intelligent lures. The bait system is a network with its own architecture and service system, built into the network of working services, which significantly increases controllability and security. The network contains static and dynamic lures with a location that monitors both external and internal interference. The honeynet provides data collection, capture, mining and control, early detection of malicious action

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Christensen, Alexander P., and Hudson Golino. "Factor or Network Model? Predictions From Neural Networks." Journal of Behavioral Data Science 1, no. 1 (2021): 85–126. http://dx.doi.org/10.35566/jbds/v1n1/p5.

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The nature of associations between variables is important for constructing theory about psychological phenomena. In the last decade, this topic has received renewed interest with the introduction of psychometric network models. In psychology, network models are often contrasted with latent variable (e.g., factor) models. Recent research has shown that differences between the two tend to be more substantive than statistical. One recently developed algorithm called the Loadings Comparison Test (LCT) was developed to predict whether data were generated from a factor or small-world network model.

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Liu, Fengqing, and Weiguang He. "Hose-model network virtualization in flexgrid optical networks." Journal of Optical Communications and Networking 12, no. 3 (2020): 13. http://dx.doi.org/10.1364/jocn.374220.

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Singh, Samayveer, Satish Chand, and Bijendra Kumar. "Multilevel heterogeneous network model for wireless sensor networks." Telecommunication Systems 64, no. 2 (2016): 259–77. http://dx.doi.org/10.1007/s11235-016-0174-2.

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Bede, Zsuzsanna, Tamás Péter, and Ferenc Szauter. "Variable network model." IFAC Proceedings Volumes 46, no. 25 (2013): 173–77. http://dx.doi.org/10.3182/20130916-2-tr-4042.00026.

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Jin, Xue-Guang, Guo-Chu Shou, Yi-Hong Hu, and Zhi-Gang Guo. "Service entity network virtualization architecture and model." Modern Physics Letters B 31, no. 19-21 (2017): 1740095. http://dx.doi.org/10.1142/s0217984917400954.

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Communication network can be treated as a complex network carrying a variety of services and service can be treated as a network composed of functional entities. There are growing interests in multiplex service entities where individual entity and link can be used for different services simultaneously. Entities and their relationships constitute a service entity network. In this paper, we introduced a service entity network virtualization architecture including service entity network hierarchical model, service entity network model, service implementation and deployment of service entity netwo

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Landro, Nicola, Ignazio Gallo, and Riccardo La Grassa. "Is One Teacher Model Enough to Transfer Knowledge to a Student Model?" Algorithms 14, no. 11 (2021): 334. http://dx.doi.org/10.3390/a14110334.

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Nowadays, the transfer learning technique can be successfully applied in the deep learning field through techniques that fine-tune the CNN’s starting point so it may learn over a huge dataset such as ImageNet and continue to learn on a fixed dataset to achieve better performance. In this paper, we designed a transfer learning methodology that combines the learned features of different teachers to a student network in an end-to-end model, improving the performance of the student network in classification tasks over different datasets. In addition to this, we tried to answer the following questi

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Wen, Hui, Tao Yan, Zhiqiang Liu, and Deli Chen. "Integrated neural network model with pre-RBF kernels." Science Progress 104, no. 3 (2021): 003685042110261. http://dx.doi.org/10.1177/00368504211026111.

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To improve the network performance of radial basis function (RBF) and back-propagation (BP) networks on complex nonlinear problems, an integrated neural network model with pre-RBF kernels is proposed. The proposed method is based on the framework of a single optimized BP network and an RBF network. By integrating and connecting the RBF kernel mapping layer and BP neural network, the local features of a sample set can be effectively extracted to improve separability; subsequently, the connected BP network can be used to perform learning and classification in the kernel space. Experiments on an

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Rak, Rafał, and Ewa Rak. "The Fractional Preferential Attachment Scale-Free Network Model." Entropy 22, no. 5 (2020): 509. http://dx.doi.org/10.3390/e22050509.

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Many networks generated by nature have two generic properties: they are formed in the process of preferential attachment and they are scale-free. Considering these features, by interfering with mechanism of the preferential attachment, we propose a generalisation of the Barabási–Albert model—the ’Fractional Preferential Attachment’ (FPA) scale-free network model—that generates networks with time-independent degree distributions p ( k ) ∼ k − γ with degree exponent 2 < γ ≤ 3 (where γ = 3 corresponds to the typical value of the BA model). In the FPA model, the element controlling the network

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M. Habash, Rania, and Mahmood Khalel. "ZERO TRUST SECURITY MODEL FOR ENTERPRISE NETWORKS." Iraqi Journal of Information and Communication Technology 6, no. 2 (2023): 68–77. http://dx.doi.org/10.31987/ijict.6.2.223.

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Zero Trust (ZT) is security model and follow the concept of ânever trust, always verifyâ. ZT require to strict identity verification for devise and clients trying to access recourses on private networks regardless of whether they are sitting within or outside the networks. As opposed to perimeter-based architecture, which makes the assumption that all internal network parties are trusted and all external network parties are untrusted. In enterprise network the internal network parties is automatically seen as trusted entities granting them access to network resources. The insider threat acto

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Treur, Jan. "On the applicability of Network-Oriented Modelling based on temporal-causal networks: why network models do not just model networks." Journal of Information and Telecommunication 1, no. 1 (2017): 23–40. http://dx.doi.org/10.1080/24751839.2017.1295653.

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Kumari, Seema, and Dr M. U. Kharat. "Delay analysis of multihop wireless ad hoc network using queuing network model." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 4 (2013): 1503–9. http://dx.doi.org/10.24297/ijct.v10i4.3250.

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Ad hoc wireless network is a self configurable infrastructure less network. The lack of infrastructure support in ad hoc networks makes them useful in various applications such as at the disaster site, highway, vast forest, battle field, oceans, etc. Since there is no centralized control in ad hoc network, each node can act as a source, a destination or a router. Ad hoc wireless networks therefore, experience congestion more than the wired networks, leading to jitter and high end to end delays even for moderate traffic which may lead to performance degradation. So it is crucial to analyze the

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Yan, Li. "HKad: A Hierarchical Kademlia Model." Applied Mechanics and Materials 457-458 (October 2013): 834–38. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.834.

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This paper proposed a new structural overlay model HKad. This model employs layered structure, and when constructing the network logical topology of peer nodes, it can detect rock-bottom physical network logical topology fast and efficiently and make it suitable to the rock-bottom network environment, and make the former be consistent to the latter as much as possible and then improve the whole performance of the systems. The goal is to construct a topology-aware overlay network, and improve the finding speed of information, and reduce unnecessary data transmission in networks.

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Yan, Li, and Ying Fang Li. "HChord: A Hierarchical Chord Model." Advanced Materials Research 756-759 (September 2013): 1916–20. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1916.

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This paper proposed a new structural overlay model HChord. This model employs layered structure, and when constructing the network logical topology of peer nodes, it can detect rock-bottom physical network logical topology fast and efficiently and make it suitable to the rock-bottom network environment, and make the former be consistent to the latter as much as possible and then improve the whole performance of the systems. The goal is to construct a topology-aware overlay network, and improve the finding speed of information, and reduce unnecessary data transmission in networks.

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Henrique, Humberto M., Enrique L. Lima, and Dale E. Seborg. "Model structure determination in neural network models." Chemical Engineering Science 55, no. 22 (2000): 5457–69. http://dx.doi.org/10.1016/s0009-2509(00)00170-6.

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Hu, Wenjin, Jiawei Xiong, Ning Wang, Feng Liu, Yao Kong, and Chaozhong Yang. "Integrated Model Text Classification Based on Multineural Networks." Electronics 13, no. 2 (2024): 453. http://dx.doi.org/10.3390/electronics13020453.

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Based on the original deep network architecture, this paper replaces the deep integrated network by integrating shallow FastText, a bidirectional gated recurrent unit (GRU) network and the convolutional neural networks (CNNs). In FastText, word embedding, 2-grams and 3-grams are combined to extract text features. In recurrent neural networks (RNNs), a bidirectional GRU network is used to lessen information loss during the process of transmission. In CNNs, text features are extracted using various convolutional kernel sizes. Additionally, three optimization algorithms are utilized to improve th

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Chakrasali, Sadashiva Veerappa, Krishnappa Indira, Sunitha Yariyur Narasimhaiah, and Shadaksharaiah Chandraiah. "Performance analysis of different intonation models in Kannada speech synthesis." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (2022): 243–52. https://doi.org/10.11591/ijeecs.v26.i1.pp243-252.

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Text to speech (TTS) is a system that generates artificial speech from text input. The prosodic models used improve the quality of the synthesized speech especially naturalness and intelligibility. The prosody involves intonation, intonation refers to the variations in the pitch frequency (F0) with respect to time in an utterance. This work mainly concentrates on building feedback neural network model to predict F0 contour in the utterances using Fujisaki intonation model parameters as the input features to the network since the Fujisaki intonation model is data driven and not a rule based one

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Mahat, Norpah, Nor Idayunie Nording, Jasmani Bidin, Suzanawati Abu Hasan, and Teoh Yeong Kin. "Artificial Neural Network (ANN) to Predict Mathematics Students’ Performance." Journal of Computing Research and Innovation 7, no. 1 (2022): 29–38. http://dx.doi.org/10.24191/jcrinn.v7i1.264.

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Predicting students’ academic performance is very essential to produce high-quality students. The main goal is to continuously help students to increase their ability in the learning process and to help educators as well in improving their teaching skills. Therefore, this study was conducted to predict mathematics students’ performance using Artificial Neural Network (ANN). The secondary data from 382 mathematics students from UCI Machine Learning Repository Data Sets used to train the neural networks. The neural network model built using nntool. Two inputs are used which are the first and the

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Novokhrestov, Aleksey, Anton Konev, and Alexander Shelupanov. "Model of Threats to Computer Network Software." Symmetry 11, no. 12 (2019): 1506. http://dx.doi.org/10.3390/sym11121506.

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This article highlights the issue of identifying information security threats to computer networks. The aim of the study is to increase the number of identified threats. Firstly, it was carried out the analysis of computer network models used to identify threats, as well as in approaches to building computer network threat models. The shortcomings that need to be corrected are highlighted. On the basis of the mathematical apparatus of attributive metagraphs, a computer network model is developed that allows to describe the software components of computer networks and all possible connections b

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45

SHANKER, O., and TAD HOGG. "EPIDEMIOLOGY MODEL ON SHORTCUT AND SMALL WORLD NETWORKS." Modern Physics Letters B 23, no. 10 (2009): 1249–62. http://dx.doi.org/10.1142/s0217984909019387.

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Abstract (sommario):

We show that the behavior of an epidemiology model depends sensitively on the shortcut density in the shortcut network. This is consistent with an earlier work on other processes on the shortcut network. We analytically study the reason for the sensitivity. The shortcut network is similar to the small world network, and it has the advantage that the model dependence on the shortcut density can be analytically studied. The model would be relevant to the spread of diseases in human, animal, plant or other populations, to the spread of viruses in computer networks, or to the spread of social cont

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Dan Tian, Dan Tian, Yong-Jie Xu Dan Tian, Tong-Lei Qu Yong-Jie Xu, Rong-Guang Jia Tong-Lei Qu, Hao Zhang Rong-Guang Jia, and Wen-Jie Song Hao Zhang. "A Bayesian Network Model for Rough Estimations of Casualties by Strong Earthquakes in Emergency Mode." 電腦學刊 33, no. 6 (2022): 083–90. http://dx.doi.org/10.53106/199115992022123306007.

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<p>Rough estimations in emergency mode are now playing an important role in making key decisions for managing disasters including search and rescue. Most of the studies only paid attention to the earthquakes and ignored the presence of disaster chains and the hazard interactions in earthquakes. Bayesian Networks are ideal tools to explore the causal relationships between events, combine prior knowledge and observed data, and are integrated to solve uncertain problems. In such situations, we present improvements based on a Bayesian Network Model in approaches to estimations of casualties

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Moeller, Joe. "Noncommutative network models." Mathematical Structures in Computer Science 30, no. 1 (2019): 14–32. http://dx.doi.org/10.1017/s0960129519000161.

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AbstractNetwork models, which abstractly are given by lax symmetric monoidal functors, are used to construct operads for modeling and designing complex networks. Many common types of networks can be modeled with simple graphs with edges weighted by a monoid. A feature of the ordinary construction of network models is that it imposes commutativity relations between all edge components. Because of this, it cannot be used to model networks with bounded degree. In this paper, we construct the free network model on a given monoid, which can model networks with bounded degree. To do this, we general

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Liu, Yong Kui, Lin Zhang, Fei Tao, and Long Wang. "An Evolving Web Service Interaction Network Model." Applied Mechanics and Materials 610 (August 2014): 559–67. http://dx.doi.org/10.4028/www.scientific.net/amm.610.559.

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With the rapid growth of Web services on the Internet, the atomic Web services as nodes and their functionality dependency relationships as edges form a complex Web service network. Various interactions between Web services can occur along the edges, such as collaboration, competition and substitution, etc. So far, however, there lack of an effective and scalable model for generating Web service interaction networks capturing the aforementioned types of interactions, which hinders the relevant researches such as development of new service composition algorithms and the investigation of evoluti

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Shin, Hyeju, Seungmin Oh, Abubakar Isah, Ibrahim Aliyu, Jaehyung Park, and Jinsul Kim. "Network Traffic Prediction Model in a Data-Driven Digital Twin Network Architecture." Electronics 12, no. 18 (2023): 3957. http://dx.doi.org/10.3390/electronics12183957.

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Abstract (sommario):

The proliferation of immersive services, including virtual reality/augmented reality, holographic content, and the metaverse, has led to an increase in the complexity of communication networks, and consequently, the complexity of network management. Recently, digital twin network technology, which applies digital twin technology to the field of communication networks, has been predicted to be an effective means of managing complex modern networks. In this paper, a digital twin network data pipeline architecture is proposed that demonstrates an integrated structure for flow within the digital t

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Zhu, Jin, Liang Tang, Hongsheng Xi, and Zhenghuan Zhang. "Reliability Analysis of Wireless Sensor Networks Using Markovian Model." Journal of Applied Mathematics 2012 (2012): 1–21. http://dx.doi.org/10.1155/2012/760359.

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Abstract (sommario):

This paper investigates reliability analysis of wireless sensor networks whose topology is switching among possible connections which are governed by a Markovian chain. We give the quantized relations between network topology, data acquisition rate, nodes' calculation ability, and network reliability. By applying Lyapunov method, sufficient conditions of network reliability are proposed for such topology switching networks with constant or varying data acquisition rate. With the conditions satisfied, the quantity of data transported over wireless network node will not exceed node capacity such

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