Relevant bibliographies by topics / Multilayered network

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multilayered network'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Multilayered network"

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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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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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Bhatia, Sanjiv K., and A. G. Starling. "Multilayered illiac network scheme." ACM SIGARCH Computer Architecture News 15, no. 4 (1987): 23–31. http://dx.doi.org/10.1145/36974.36978.

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Kaneko, Yoshihisa, S. Hirota, and Satoshi Hashimoto. "Discrete Dislocation Dynamics Simulation on Strengths of Dislocation Network Stacks in Multilayered Structures." Key Engineering Materials 353-358 (September 2007): 1086–89. http://dx.doi.org/10.4028/www.scientific.net/kem.353-358.1086.

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Strengths of multilayered structures have been investigated using three-dimensional discrete dislocation dynamics (DDD) simulation. The multilayered structure was modeled as a stack of misfit dislocation networks which must exist at an interface between adjoining crystals having different lattice constants. Passages of a single mobile dislocation through several kinds of network stacks were simulated. The critical stress required for the dislocation passage depended on the dislocation spacing of the network, the number of network sheet and the spacing between network sheets.
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Sun, Hong Wei, and Xiao Chun Wang. "Analysis of Microstrip Filter Circuit Based on Network Port Model of MOM." Applied Mechanics and Materials 303-306 (February 2013): 1859–63. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1859.

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By combining the method of moment (MoM) and the method of network analysis, we analyze the microstrip filter circuits. First, we deduce and calculate the closed form multilayered Green’s function by using the discrete complex image method. Then, we apply the multilayered Green’s function into the method of moment (MoM) and by using the multi-port network theory, we get the networks parameters. At last, the numerical result proves the method’s accuracy and validity.
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Psaltis, D., A. Sideris, and A. A. Yamamura. "A multilayered neural network controller." IEEE Control Systems Magazine 8, no. 2 (1988): 17–21. http://dx.doi.org/10.1109/37.1868.

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Marchal, Nicolas, Tristan da Câmara Santa Clara Gomes, Flavio Abreu Araujo, and Luc Piraux. "Giant Magnetoresistance and Magneto-Thermopower in 3D Interconnected NixFe1−x/Cu Multilayered Nanowire Networks." Nanomaterials 11, no. 5 (2021): 1133. http://dx.doi.org/10.3390/nano11051133.

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The versatility of the template-assisted electrodeposition technique to fabricate complex three-dimensional networks made of interconnected nanowires allows one to easily stack ferromagnetic and non-magnetic metallic layers along the nanowire axis. This leads to the fabrication of unique multilayered nanowire network films showing giant magnetoresistance effect in the current-perpendicular-to-plane configuration that can be reliably measured along the macroscopic in-plane direction of the films. Moreover, the system also enables reliable measurements of the analogous magneto-thermoelectric pro
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Vasiliadis, D. C., G. E. Rizos, and C. Vassilakis. "Performance Analysis of Multilayered Multipriority Asymmetric-Sized Delta Networks." Journal of Computer Networks and Communications 2011 (2011): 1–12. http://dx.doi.org/10.1155/2011/723102.

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The performance of multilayered asymmetric-sized finite-buffered delta networks supporting multiclass routing traffic is presented and analyzed in the uniform traffic conditions under various loads using simulations. The rationale behind introducing asymmetric-sized buffered systems is to have a better exploitation of available buffer spaces, while the implementation of multilayered architecture is applied in order to further improve the overall performance of network. The findings of this performance evaluation can be used by network designers for drawing optimal configurations while setting
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Kondo, Tadashi, Junji Ueno, and Kazuya Kondo. "Revised GMDH-Type Neural Networks Using AIC or PSS Criterion and Their Application to Medical Image Recognition." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 3 (2005): 257–67. http://dx.doi.org/10.20965/jaciii.2005.p0257.

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This study deals with the revised Group Method of Data Handling (GMDH)-type neural network algorithm using prediction error criterion defined as Prediction Sum of Squares (PSS) or Akaike's Information Criterion (AIC). The revised GMDH-type neural network algorithm generates optimum multilayered neural network architectures fitting the complexity of nonlinear systems using heuristic self-organization. The revised GMDH-type neural networks self-select the number of layers, optimum neuronal architectures, and useful input variables to minimize prediction error criterion defined as PSS or AIC. Thi
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Rippert, E. D., and S. Lomatch. "A multilayered superconducting neural network implementation." IEEE Transactions on Appiled Superconductivity 7, no. 2 (1997): 3442–45. http://dx.doi.org/10.1109/77.622126.

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Dissertations / Theses on the topic "Multilayered network"

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Viljoen, Nadia M. "Quantifying supply chain vulnerability using a multilayered complex network perspective." Thesis, University of Pretoria, 2018. http://hdl.handle.net/2263/63990.

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Today's supply chains face increasing volatility on many fronts. From the shop-floor where machines break and suppliers fail to the boardrooms where unanticipated price inflation erodes profi tability. Turbulence is the new normal. To remain competitive and weather these (daily) storms, supply chains need to move away from an effi ciency mindset towards a resilience mindset. For over a little more than a decade industry and academia have awakened to this reality. Academic literature and case studies show that there is no longer a shortage of resilience strategies and designs. Unfortunately,
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Srivastava, Atul Carleton University Dissertation Management Studies. "Identifying stock winners and losers; a multilayered artificial neural network approach." Ottawa, 1996.

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Aram, Michael, and Gustaf Neumann. "Multilayered analysis of co-development of business information systems." Springer London, 2015. http://dx.doi.org/10.1186/s13174-015-0030-8.

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Business information systems (BIS) comprise technological (e.g. programs), informational (e.g. content) and social artifacts (e.g. collaboration structures). Typically, such systems are constantly and collectively developed (co-developed) further by a variety of individuals within the organization. By recognizing these varying types of actors (concerning their goals, technical expertise and language means) and their predominantly developed artifact type, one can distinguish two types of subsystems: technical subsystems wherein the development of the system behavior is conducted by software dev
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Larsson, Marcus, and Christoffer Möckelind. "The effects of Deep Belief Network pre-training of a Multilayered perceptron under varied labeled data conditions." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187374.

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Sometimes finding labeled data for machine learning tasks is difficult. This is a problem for purely supervised models like the Multilayered perceptron(MLP). A Discriminative Deep Belief Network(DDBN) is a semi-supervised model that is able to use both labeled and unlabeled data. This research aimed to move towards a rule of thumb of when it is beneficial to use a DDBN instead of an MLP, given the proportions of labeled and unlabeled data. Several trials with different amount of labels, from the MNIST and Rectangles-Images datasets, were conducted to compare the two models. It was found that f
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Ellerbrock, Thomas M. "Multilayer neural networks learnability, network generation, and network simplification /." [S.l. : s.n.], 1999. http://deposit.ddb.de/cgi-bin/dokserv?idn=958467897.

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Hultin, Alex. "Sustaining interdisciplinary research : a multilayer perspective." Thesis, University of Bath, 2018. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.767571.

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Interdisciplinary Research (IDR) has received a lot of attention from academics, policy-makers, and decision-makers alike. RCUK invests £3 billion in research grants each year (RCUK 2017); half of the grants are provided to investigators who hail from different departments. There is mounting awareness of the challenges facing IDR, and a large body of literature trying to establish how IDR can be analysed (Davidson 2015, Yegros-Yegros, Rafols et al. 2015). Of these, the majority have been qualitative studies and it has been noticed that there is a distinct lack of quantitative studies that can
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Darabi, Sahneh Faryad. "Spreading processes over multilayer and interconnected networks." Diss., Kansas State University, 2014. http://hdl.handle.net/2097/18179.

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Doctor of Philosophy<br>Department of Electrical and Computer Engineering<br>Caterina Scoglio<br>Society increasingly depends on networks for almost every aspect of daily life. Over the past decade, network science has flourished tremendously in understanding, designing, and utilizing networks. Particularly, network science has shed light on the role of the underlying network topology on the dynamic behavior of complex systems, including cascading failure in power-grids, financial contagions in trade market, synchronization, spread of social opinion and trends, product adoption and market pene
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Catanese, Salvatore Amato. "New perspectives in criminal network analysis: multilayer networks, time evolution, and visualization." Doctoral thesis, Università di Catania, 2017. http://hdl.handle.net/10761/3793.

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The work presented in this Dissertation reflects a long-term human, professional and cultural path started some years ago when I first developed LogAnalysis, a tool for the analysis and visualization of criminal and social networks. Since then, I devoted myself to the development of frameworks, algorithms and techniques for supporting intelligence and law enforcement agencies in the task of unveiling the CN structure hidden in communication data, identifying the target offenders for their removal or selecting effective strategies to disrupt a criminal organization. In a natural way, I successi
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Afsarmanesh, Tehrani Nazanin. "Clustering Multilayer Networks." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-279745.

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Detecting community structure is an important methodology to study complex networks. Community detection methods can be divided into two main categories: partitioning methods and overlapping clustering methods. In partitioning methods, each node can belong to at most one community while overlapping clustering methods allow communities with overlapping nodes as well. Community detection is not only a problem in single networks today, but also in multilayer networks where several networks with the same participants are considered at the same time. In recent years, several methods have been propo
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Pekkanen, Peter, and David Söderman. "Network Hardening of Multilayer Switch." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54674.

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In today’s computer networking and industrial networking, security holds a significant role in keeping the entire network safe from malicious users or attackers from remote locations. Using vulnerability scanners and a port scanner it is possible to detect vulnerabilities on the network equipment before an attacker finds the vulnerability and exploits it. In this thesis network hardening has been conducted through a case study on a multilayer switch, with the intent to discover vulnerabilities related to gaining unauthorized access to the device during operation. The tools included in this pro
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Books on the topic "Multilayered network"

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Stübing, Hagen. Multilayered Security and Privacy Protection in Car-to-X Networks: Solutions from Application down to Physical Layer. Springer Fachmedien Wiesbaden, 2013.

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Krawczak, Maciej. Multilayer Neural Networks. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00248-4.

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Masih, Lawrence. Associative recall in multilayered logical neural networks. Brunel University, 1990.

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De Domenico, Manlio. Multilayer Networks: Analysis and Visualization. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-75718-2.

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McGee, Fintan, Mohammad Ghoniem, Benoît Otjacques, et al. Visual Analysis of Multilayer Networks. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-02608-9.

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Webb, Karen. Building Cisco multilayer switched networks. Cisco Press, 2000.

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C, Bass John, and Robinson James E. III, eds. Building Cisco multilayer switching networks. McGraw-Hill, 2000.

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Balaji, Sivasubramanian, and Frahim Erum, eds. Building Cisco multilayer switched networks (BCMSN). 4th ed. Cisco Press, 2007.

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Thomas, Thomas M. BCMSN: Building Cisco multilayer switching networks. McGraw-Hill, 2000.

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Luttrell, Stephen P. Self-supervision in multilayer adaptive networks. HMSO, 1991.

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Book chapters on the topic "Multilayered network"

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Bródka, Piotr, and Przemyslaw Kazienko. "Multilayered Social Networks." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_239.

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Yu, Shao-Bo, Shou-Ren Hu, and Jun-Yong Yan. "Quantum Learning Algorithm for Multilayered Neural Network." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_79.

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Iwami, Ken’ichi, Nobuyuki Matsui, and Toshiharu Araki. "Fluctuated-Threshold Effect in Multilayered Neural Network." In ICANN ’94. Springer London, 1994. http://dx.doi.org/10.1007/978-1-4471-2097-1_129.

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Biscontini, Bruno, Markus Burger, and Peter Russer. "Network Methods Applied to Multilayered Cylindrical Radiating Structures." In Springer Proceedings in Physics. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-07221-9_12.

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Stübing, Hagen. "Network Layer Security: Group Privacy Protocols." In Multilayered Security and Privacy Protection in Car-to-X Networks. Springer Fachmedien Wiesbaden, 2013. http://dx.doi.org/10.1007/978-3-658-02531-1_5.

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Rácz, János, and Tamás Klotz. "Pattern Recognition with a Multilayered Neural Network Trained with Dynamic Competitive Learning." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_106.

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Hartono, Pitoyo, Paul Hollensen, and Thomas Trappenberg. "Visualizing Hierarchical Representation in a Multilayered Restricted RBF Network." In Artificial Neural Networks and Machine Learning – ICANN 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11179-7_43.

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Verma, Rohit, and Punit Gupta. "Neural Network-Based Resource Allocation Model in Multilayered Cloud." In Machine Learning and Optimization Models for Optimization in Cloud. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003185376-3.

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Ramli, Dzati Athiar, Ahmad Fauzan Kadmin, Mohd Yusoff Mashor, Nor Ashidi, and Mat Isa. "Diagnosis of Cervical Cancer Using Hybrid Multilayered Perceptron (HMLP) Network." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30132-5_82.

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Rajagopal, Manikandan, and S. Ramkumar. "A Multilayered Feed-Forward Neural Network Architecture for Rainfall Forecasting." In Artificial Intelligence in Forecasting. CRC Press, 2024. http://dx.doi.org/10.1201/9781003399292-2.

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Conference papers on the topic "Multilayered network"

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Pacharintanakul, Peera, and David Tipper. "Differentiated crosslayer network mapping in multilayered network architectures." In 2010 14th International Telecommunications Network Strategy and Planning Symposium (NETWORKS). IEEE, 2010. http://dx.doi.org/10.1109/netwks.2010.5624908.

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Piergigli, Mauro, Roberto Vercelli, and A. Zucchinali. "Management issues in multilayered network architectures." In Advanced Imaging and Network Technologies, edited by Roberto Vercelli. SPIE, 1996. http://dx.doi.org/10.1117/12.261242.

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Ali, M. S. A. Megat, C. Z. A. Che Zainal, A. Husman, M. F. Saaid, M. Z. H. Noor, and A. H. Jahidin. "Detection of cardiomyopathy using multilayered perceptron network." In 2012 IEEE 8th International Colloquium on Signal Processing & its Applications (CSPA). IEEE, 2012. http://dx.doi.org/10.1109/cspa.2012.6194764.

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Jagota and Jakubowicz. "Knowledge representation in a multilayered Hopfield network." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118600.

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Nakauchi, S., and S. Usui. "Multilayered neural network models for color blindness." In 1991 IEEE International Joint Conference on Neural Networks. IEEE, 1991. http://dx.doi.org/10.1109/ijcnn.1991.170446.

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Rajan, P., and S. Varatha Rajan. "Network supporting multilayered quality of service routing in Wireless Sensor Networks." In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT 2011). IEEE, 2011. http://dx.doi.org/10.1109/icetect.2011.5760268.

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Ali, M. S. A. M., N. F. Shaari, N. Julai, et al. "Robust arrhythmia classifier using hybrid multilayered perceptron network." In 2013 IEEE 9th International Colloquium on Signal Processing & its Applications (CSPA). IEEE, 2013. http://dx.doi.org/10.1109/cspa.2013.6530061.

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Somasiri, N. P. "Neural network modeller for multilayered microstrip patch antennas." In Twelfth International Conference on Antennas and Propagation (ICAP 2003). IEE, 2003. http://dx.doi.org/10.1049/cp:20030150.

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Medicherla, Abhishek Bharadwaj, Rithvik Vukka, Sampat Srivatsav Jakkinapalli, and Deepthi L. R. "Influential Node Identification on an Multilayered Attributed Network." In IC3 2023: 2023 Fifteenth International Conference on Contemporary Computing. ACM, 2023. http://dx.doi.org/10.1145/3607947.3608084.

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Chen, Zhiyi, Xinghuo Yu, and Guanghui Wen. "Self-triggered Consensus Control for Multilayered Cluster Network." In 2021 International Conference on Neuromorphic Computing (ICNC). IEEE, 2021. http://dx.doi.org/10.1109/icnc52316.2021.9607929.

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Reports on the topic "Multilayered network"

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Tarasenko, Andrii O., Yuriy V. Yakimov, and Vladimir N. Soloviev. Convolutional neural networks for image classification. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3682.

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This paper shows the theoretical basis for the creation of convolutional neural networks for image classification and their application in practice. To achieve the goal, the main types of neural networks were considered, starting from the structure of a simple neuron to the convolutional multilayer network necessary for the solution of this problem. It shows the stages of the structure of training data, the training cycle of the network, as well as calculations of errors in recognition at the stage of training and verification. At the end of the work the results of network training, calculatio
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Lai, W., and D. McDysan, eds. Network Hierarchy and Multilayer Survivability. RFC Editor, 2002. http://dx.doi.org/10.17487/rfc3386.

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Markova, Oksana, Serhiy Semerikov та Maiia Popel. СoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. Sun SITE Central Europe, 2018. http://dx.doi.org/10.31812/0564/2250.

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The role of neural network modeling in the learning сontent of special course “Foundations of Mathematic Informatics” was discussed. The course was developed for the students of technical universities – future IT-specialists and directed to breaking the gap between theoretic computer science and it’s applied applications: software, system and computing engineering. CoCalc was justified as a learning tool of mathematical informatics in general and neural network modeling in particular. The elements of technique of using CoCalc at studying topic “Neural network and pattern recognition” of the sp
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Russo, Margherita, Fabrizio Alboni, Jorge Carreto Sanginés, et al. The Changing Shape of the World Automobile Industry: A Multilayer Network Analysis of International Trade in Components and Parts. Institute for New Economic Thinking Working Paper Series, 2022. http://dx.doi.org/10.36687/inetwp173.

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In 2018, after 25 years of the North America Trade Agreement (NAFTA), the United States requested new rules which, among other requirements, increased the regional con-tent in the production of automotive components and parts traded between the three part-ner countries, United States, Canada and Mexico. Signed by all three countries, the new trade agreement, USMCA, is to go into force in 2022. Nonetheless, after the 2020 Presi-dential election, the new treaty's future is under discussion, and its impact on the automo-tive industry is not entirely defined. Another significant shift in this indu
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Williams, Adam, Gabriel Birch, Susan Caskey, et al. LDRD23-0730: Invoking Multilayer Networks to Develop a Paradigm for Security Science—Summary Report. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2430222.

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Rivera-Casillas, Peter, and Ian Dettwiller. Neural Ordinary Differential Equations for rotorcraft aerodynamics. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48420.

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High-fidelity computational simulations of aerodynamics and structural dynamics on rotorcraft are essential for helicopter design, testing, and evaluation. These simulations usually entail a high computational cost even with modern high-performance computing resources. Reduced order models can significantly reduce the computational cost of simulating rotor revolutions. However, reduced order models are less accurate than traditional numerical modeling approaches, making them unsuitable for research and design purposes. This study explores the use of a new modified Neural Ordinary Differential
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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature
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Ramakrishnan, Aravind, Fangyu Liu, Angeli Jayme, and Imad Al-Qadi. Prediction of Pavement Damage under Truck Platoons Utilizing a Combined Finite Element and Artificial Intelligence Model. Illinois Center for Transportation, 2024. https://doi.org/10.36501/0197-9191/24-030.

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For robust pavement design, accurate damage computation is essential, especially for loading scenarios such as truck platoons. Studies have developed a framework to compute pavement distresses as function of lateral position, spacing, and market-penetration level of truck platoons. The established framework uses a robust 3D pavement model, along with the AASHTOWare Mechanistic–Empirical Pavement Design Guidelines (MEPDG) transfer functions to compute pavement distresses. However, transfer functions include high variability and lack physical significance. Therefore, as an improvement to effecti
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