Bibliografie tematiche / Network model

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Letteratura scientifica selezionata sul tema "Network model"

Autore: Grafiati
Pubblicato: 4 giugno 2021
Ultima modifica: 25 luglio 2025

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Articoli di riviste sul tema "Network model"

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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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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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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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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Abstract (sommario):
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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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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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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Tesi sul tema "Network model"

1

Xiaodan, Xie. "Network Interdiction Model on Interdependent Incomplete Network." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1593537784177702.

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Mao, Ai-sheng. "A Theoretical Network Model and the Incremental Hypercube-Based Networks." Thesis, University of North Texas, 1995. https://digital.library.unt.edu/ark:/67531/metadc277860/.

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The study of multicomputer interconnection networks is an important area of research in parallel processing. We introduce vertex-symmetric Hamming-group graphs as a model to design a wide variety of network topologies including the hypercube network.
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Kuehn, Daniel, and Sofia Ljunggren. "Refining a Network Model Concerning Network Security Risk Analysis." Thesis, KTH, Data- och elektroteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-154355.

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P²CySeMoL is a framework in which security risks are calculated and presented with a value referring to the probability that an attack will succeed in a system, mainly SCADA systems. This thesis covers the creation of a more granular network module for the P²CySeMoL security riskanalysis framework to better be able to represent a network in concrete modules and to enable security riskanalysis on a network infrastructure at a greater detail. This paper outlines the creation of a network module with the base in the OSI model. It is replicated in a way that the network module is an extension to t
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Gammelgård, Magnus. "The network performance assessment model." Licentiate thesis, KTH, Electrical Systems, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-1746.

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<p>The electricity distribution in Sweden is experiencing aconsiderable change in conditions by the radical change inregulation policy. The Swedish Energy Agency(Energimyndigheten) is introducing a new regulatory model, theNetwork Performance Assessment Model, as the main tool forsupervising the natural monopolies of local electricitydistribution. The new model has interesting and far-reachingconsequences for the involved parties.</p><p>This thesis investigates the implications of the newregulation and the regulatory tool, in particularly related toIT-investments evaluations in the distributio
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Arulselvan, Ashwin. "Network model for disaster management." [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0024855.

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Lundy, G. M. "Systems of communicating machines : a model for communication protocols." Diss., Georgia Institute of Technology, 1988. http://hdl.handle.net/1853/8210.

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Yao, Ye. "Ad Hoc Networks Measurement Model and Methods Based on Network Tomography." Phd thesis, Université de Technologie de Belfort-Montbeliard, 2011. http://tel.archives-ouvertes.fr/tel-00636282.

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The measurability of Mobile ad hoc network (MANET) is the precondition of itsmanagement, performance optimization and network resources re-allocations. However, MANET is an infrastructure-free, multi-hop, andself-organized temporary network, comprised of a group of mobile nodes with wirelesscommunication devices. Not only does its topology structure vary with time going by, butalso the communication protocol used in its network layer or data link layer is diverse andnon-standard.In order to solve the problem of interior links performance (such as packet loss rate anddelay) measurement in MANET
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Al-Musawi, Ahmad Jr. "COMPLEX NETWORK GROWING MODEL USING DOWNLINK MOTIFS." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3088.

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Understanding the underlying architecture of gene regulatory networks (GRNs) has been one of the major goals in systems biology and bioinformatics as it can provide insights in disease dynamics and drug development. Such GRNs are characterized by their scale-free degree distributions and existence of network motifs, which are small subgraphs of specific types and appear more abundantly in GRNs than in other randomized networks. In fact, such motifs are considered to be the building blocks of GRNs (and other complex networks) and they help achieve the underlying robustness demonstrated by most
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Draai, Kevin. "A model for assessing and reporting network performance measurement in SANReN." Thesis, Nelson Mandela Metropolitan University, 2017. http://hdl.handle.net/10948/16131.

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The performance measurement of a service provider network is an important activity. It is required for the smooth operation of the network as well as for reporting and planning. SANReN is a service provider tasked with serving the research and education network of South Africa. It currently has no structure or process for determining network performance metrics to measure the performance of its network. The objective of this study is to determine, through a process or structure, which metrics are best suited to the SANReN environment. This study is conducted in 3 phases in order to discover an
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Jones, Thomas Carroll Jr. "JigCell Model Connector: Building Large Molecular Network Models from Components." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78277.

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The ever-growing size and complexity of molecular network models makes them difficult to construct and understand. Modifying a model that consists of tens of reactions is no easy task. Attempting the same on a model containing hundreds of reactions can seem nearly impossible. We present the JigCell Model Connector, a software tool that supports large-scale molecular network modeling. Our approach to developing large models is to combine together smaller models, making the result easier to comprehend. At the base, the smaller models (called modules) are defined by small collections of reactions
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Libri sul tema "Network model"

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Dong, Yucheng, Zhaogang Ding, and Gang Kou. Social Network DeGroot Model. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0421-7.

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Wasserman, Theodore, and Lori Drucker Wasserman. Apraxia: The Neural Network Model. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-24105-5.

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Network, Canadian Model Forest. Canadian Model Forest Network achievements. Natural Resources Canada, 2006.

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Black, Uyless. OSI: A model for computer communications standards. Prentice-Hall, 1991.

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Wasserman, Theodore, and Lori Drucker Wasserman. Therapy and the Neural Network Model. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26921-0.

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Wasserman, Theodore, and Lori Wasserman. Motivation, Effort, and the Neural Network Model. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58724-6.

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Fortescue, Michael D. A neural network model of lexical organization. Continuum, 2011.

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Estrada, James. Connections: A report on network model options. Connecticut State Library, 1993.

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Christakis, Nicholas A. An empirical model for strategic network formation. National Bureau of Economic Research, 2010.

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Magedanz, Thomas. An integrated management model for intelligent networks. R. Oldenbourg, 1994.

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Capitoli di libri sul tema "Network model"

1

Stańczak, Sławomir, Marcin Wiczanowski, and Holger Boche. "Network Model." In Fundamentals of Resource Allocation in Wireless Networks. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-79386-1_4.

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Nicoletti, Bernardo. "SCOR Model." In Supply Network 5.0. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22032-6_2.

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Shekhar, Shashi, and Hui Xiong. "Network Data Model." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_876.

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Feng, Jun, and Toyohide Watanabe. "Road Network Model." In Index and Query Methods in Road Networks. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10789-9_3.

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Hainaut, Jean-Luc. "Network Data Model." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_246-2.

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Chang, Gerard J., Lirong Cui, and Frank K. Hwang. "The Network Model." In Network Theory and Applications. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4613-0273-5_7.

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Rawolle, Shaun, Muriel Wells, Louise Paatsch, Russell Tytler, and Coral Campbell. "The Network Model." In Improving Schools. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-931-8_2.

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Shekhar, Shashi, and Hui Xiong. "Roadway Network Model." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1141.

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Shekhar, Shashi, and Hui Xiong. "Spatial Network Model." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1287.

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Shekhar, Shashi, and Hui Xiong. "Transportation Network Model." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1404.

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Atti di convegni sul tema "Network model"

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Obamiyi, Stephen E., Adeola D. Adebusuyi, Abiodun Oguntimilehin, et al. "A Network Intrusion Detection Model for IoT Networks." In 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG). IEEE, 2024. http://dx.doi.org/10.1109/seb4sdg60871.2024.10629842.

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Zhang, Runbo, Tao Tao, Jibin Wang, Zhuo Chen, Qingyuan Hu, and Zhen Li. "Graph Neural Network Model Under Multi-Modal Fusion." In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS). IEEE, 2024. https://doi.org/10.1109/iciics63763.2024.10860124.

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Burgueno, Loli, Jordi Cabot, and Sebastien Gerard. "An LSTM-Based Neural Network Architecture for Model Transformations." In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 2019. http://dx.doi.org/10.1109/models.2019.00013.

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Martins, João, José M. Fonseca, Rafael Costa, et al. "Verification of railway network models with EVEREST." In MODELS '22: ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems. ACM, 2022. http://dx.doi.org/10.1145/3550355.3552439.

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Salem, Ahmed, Michael Backes, and Yang Zhang. "Get a Model! Model Hijacking Attack Against Machine Learning Models." In Network and Distributed System Security Symposium. Internet Society, 2022. http://dx.doi.org/10.14722/ndss.2022.23064.

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Kahani, Nafiseh, Mojtaba Bagherzadeh, Reza Ahmadi, and Juergen Dingel. "Leveraging modeling concepts and techniques to address challenges in network management." In 2023 ACM/IEEE 26th International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 2023. http://dx.doi.org/10.1109/models58315.2023.00021.

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Ferrandiz, Thomas, Fabrice Frances, and Christian Fraboul. "A Network Calculus Model for SpaceWire Networks." In 2011 IEEE 17th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA). IEEE, 2011. http://dx.doi.org/10.1109/rtcsa.2011.42.

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Ayoughi, Negin, Shiva Nejati, Mehrdad Sabetzadeh, and Patricio Saavedra. "Enhancing Automata Learning with Statistical Machine Learning: A Network Security Case Study." In MODELS '24: ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems. ACM, 2024. http://dx.doi.org/10.1145/3640310.3674087.

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Johrendt, Jennifer L., and Peter R. Frise. "Neural Network Bushing Model Development Using Simulation." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28103.

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Abstract (sommario):
Neural networks are computationally efficient mathematical models that can be used to model quantitative and qualitative data. A neural network can be created through training with known input and output load-deflection data such that it learns to generalize the material characteristics without over-predicting the training data and losing its ability to anticipate behavior outside the training set. The challenge in creating a neural network model of a rubber bushing in a virtual model of a prototype assembly, for instance, is the lack of a physical prototype assembly. This paper describes a me
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Dahai Han, Kai Zhang, Xinyuan Lai, and Min Zhao. "Novel network simulation model of sensor networks with complex network charateristics." In IET International Conference on Communication Technology and Application (ICCTA 2011). IET, 2011. http://dx.doi.org/10.1049/cp.2011.0713.

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Rapporti di organizzazioni sul tema "Network model"

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Yoo, Wucherl, and Alex Sim. Network Bandwidth Utilization Forecast Model on High Bandwidth Network. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1136782.

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Bierman, A., and M. Bjorklund. Network Configuration Access Control Model. RFC Editor, 2018. http://dx.doi.org/10.17487/rfc8341.

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Berger, L., C. Hopps, A. Lindem, D. Bogdanovic, and X. Liu. YANG Data Model for Network Instances. RFC Editor, 2019. http://dx.doi.org/10.17487/rfc8529.

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Berger, L., C. Hopps, A. Lindem, D. Bogdanovic, and X. Liu. YANG Model for Logical Network Elements. RFC Editor, 2019. http://dx.doi.org/10.17487/rfc8530.

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Bhushan, Shanti, Greg Burgreen, Wesley Brewer, and Ian Dettwiller. Assessment of neural network augmented Reynolds averaged Navier Stokes turbulence model in extrapolation modes. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49702.

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A machine-learned model enhances the accuracy of turbulence transport equations of RANS solver and applied for periodic hill test case. The accuracy is investigated in extrapolation modes. A parametric study is also performed to understand the effect of network hyperparameters on training and model accuracy and to quantify the uncertainty in model accuracy due to the non-deterministic nature of the neural network training. For any network, less than optimal mini-batch size results in overfitting, and larger than optimal reduces accuracy. Data clustering is an efficient approach to prevent the
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Moreaux, Alexandre, Samantha Gamboa, David Griffith, and Richard Rouil. UE-to-Network Relay Model B Discovery in ProSe-Enabled LTE Networks. National Institute of Standards and Technology, 2021. http://dx.doi.org/10.6028/nist.tn.2149.

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Makedonska, Nataliia, Elchin Jararov, and Lianjie Huang. Flow Simulation Using Discrete Fracture Network Model. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1469490.

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Zisiadis, D., S. Kopsidas, M. Tsavli, and G. Cessieux, eds. The Network Trouble Ticket Data Model (NTTDM). RFC Editor, 2011. http://dx.doi.org/10.17487/rfc6137.

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Bierman, A., and M. Bjorklund. Network Configuration Protocol (NETCONF) Access Control Model. RFC Editor, 2012. http://dx.doi.org/10.17487/rfc6536.

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Konrad, A. M., M. Perez, J. Rivera, et al. Investigation of network-based information system model. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/409897.

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