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Journal articles on the topic 'Multilayered network'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 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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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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4

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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6

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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7

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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11

Compain, Jean-Daniel, Koji Nakabayashi, and Shin-ichi Ohkoshi. "A Polyoxometalate–Cyanometalate Multilayered Coordination Network." Inorganic Chemistry 51, no. 9 (2012): 4897–99. http://dx.doi.org/10.1021/ic300263f.

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12

King Hann Lim, Kah Phooi Seng, Li-Minn Ang, and Siew Wen Chin. "Lyapunov Theory-Based Multilayered Neural Network." IEEE Transactions on Circuits and Systems II: Express Briefs 56, no. 4 (2009): 305–9. http://dx.doi.org/10.1109/tcsii.2009.2015400.

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13

Koene, Randal A., and Yoshio Takane. "Discriminant Component Pruning: Regularization and Interpretation of Multilayered Backpropagation Networks." Neural Computation 11, no. 3 (1999): 783–802. http://dx.doi.org/10.1162/089976699300016665.

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Neural networks are often employed as tools in classification tasks. The use of large networks increases the likelihood of the task's being learned, although it may also lead to increased complexity. Pruning is an effective way of reducing the complexity of large networks. We present discriminant components pruning (DCP), a method of pruning matrices of summed contributions between layers of a neural network. Attempting to interpret the underlying functions learned by the network can be aided by pruning the network. Generalization performance should be maintained at its optimal level following

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14

Ramadoss, Jagadeesh, and Arumugam Sonachalam. "Multilayered nano Ti3C2Tx electrode: An ultrasensitive electrochemical sensor for rutin antioxidant detection." Malaysian NANO-An International Journal 3, no. 1 (2023): 44–54. http://dx.doi.org/10.22452/mnij.vol3no1.4.

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Multilayer two-dimensional (2D) structures (MXenes) provide promising advantages in biomedical applications. Using SEM and XRD characterization techniques, the synthesized substance was identified. Our results show that Ti3C2Tx-GCE possesses a significant number of active sites, enhancing its electrocatalytic activity and electrochemical sensing abilities for RT oxidation. The multilayered Ti3C2Tx-GCE that was produced had good electrochemical properties and acceptable pore structures. Additionally, it exhibited remarkable linearity starting from 1 to 10 μM and demonstrated high sensitivity fo

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15

Gao, Jianxi, Daqing Li, and Shlomo Havlin. "From a single network to a network of networks." National Science Review 1, no. 3 (2014): 346–56. http://dx.doi.org/10.1093/nsr/nwu020.

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Abstract Network science has attracted much attention in recent years due to its interdisciplinary applications. We witnessed the revolution of network science in 1998 and 1999 started with small-world and scale-free networks having now thousands of high-profile publications, and it seems that since 2010 studies of ‘network of networks’ (NON), sometimes called multilayer networks or multiplex, have attracted more and more attention. The analytic framework for NON yields a novel percolation law for n interdependent networks that shows that percolation theory of single networks studied extensive

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16

Wang, Bingbo, Xiujuan Ma, Cunchi Wang, Mingjie Zhang, Qianhua Gong, and Lin Gao. "Conserved Control Path in Multilayer Networks." Entropy 24, no. 7 (2022): 979. http://dx.doi.org/10.3390/e24070979.

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The determination of directed control paths in complex networks is important because control paths indicate the structure of the propagation of control signals through edges. A challenging problem is to identify them in complex networked systems characterized by different types of interactions that form multilayer networks. In this study, we describe a graph pattern called the conserved control path, which allows us to model a common control structure among different types of relations. We present a practical conserved control path detection method (CoPath), which is based on a maximum-weighte

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17

Barkai, E., D. Hansel, and I. Kanter. "Statistical mechanics of a multilayered neural network." Physical Review Letters 65, no. 18 (1990): 2312–15. http://dx.doi.org/10.1103/physrevlett.65.2312.

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18

Barkai, E., D. Hansel, and I. Kanter. "Statistical Mechanics of a Multilayered Neural Network." Physical Review Letters 65, no. 25 (1990): 3210. http://dx.doi.org/10.1103/physrevlett.65.3210.2.

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19

Patil, P. B. "Multilayered network for LPC based speech recognition." IEEE Transactions on Consumer Electronics 44, no. 2 (1998): 435–38. http://dx.doi.org/10.1109/30.681960.

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20

Matsui, Nobuyuki, and Ken'ichi Iwami. "Multilayered Neural Network with Fluctuated-Threshold Neuron." IEEJ Transactions on Electronics, Information and Systems 114, no. 11 (1994): 1208–13. http://dx.doi.org/10.1541/ieejeiss1987.114.11_1208.

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21

del Genio, Charo I., Jesús Gómez-Gardeñes, Ivan Bonamassa, and Stefano Boccaletti. "Synchronization in networks with multiple interaction layers." Science Advances 2, no. 11 (2016): e1601679. http://dx.doi.org/10.1126/sciadv.1601679.

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The structure of many real-world systems is best captured by networks consisting of several interaction layers. Understanding how a multilayered structure of connections affects the synchronization properties of dynamical systems evolving on top of it is a highly relevant endeavor in mathematics and physics and has potential applications in several socially relevant topics, such as power grid engineering and neural dynamics. We propose a general framework to assess the stability of the synchronized state in networks with multiple interaction layers, deriving a necessary condition that generali

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22

Miller, M., and E. N. Miranda. "STABILITY OF MULTILAYERED NEURAL NETWORKS." International Journal of Neural Systems 02, no. 01n02 (1991): 143–46. http://dx.doi.org/10.1142/s0129065791000133.

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Stability of a multilayered neural network architecture against synaptic changes has been studied numerically. We have found that the average change goes to zero as the number N of input neurons is N≫1. If a fixed fraction of output mistakes is allowed, then the synapses may be changed within some limits even for large N.

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23

Zeng, Pan. "Neural Computing in Mechanics." Applied Mechanics Reviews 51, no. 2 (1998): 173–97. http://dx.doi.org/10.1115/1.3098995.

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Recently, the artificial neural network has experienced a surge in popularity and is now one of the most rapidly expanding areas of research across many disciplines. The main reason is in its powerful and adaptive abilities to treat various complex problems. One can be sure that with its further developments, neural networks will strongly impact many conventional disciplines from the standpoint of methodology. In the field of mechanics, the research and application of both neural network and revolutionary computing are especially active and successful. The back propagated multilayered network

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24

Gao, Shengbo, Mingyuan Ma, Bin Liang, Yuan Du, Li Du, and Kunji Chen. "Audio Signal-Stimulated Multilayered HfOx/TiOy Spiking Neuron Network for Neuromorphic Computing." Nanomaterials 14, no. 17 (2024): 1412. http://dx.doi.org/10.3390/nano14171412.

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As the key hardware of a brain-like chip based on a spiking neuron network (SNN), memristor has attracted more attention due to its similarity with biological neurons and synapses to deal with the audio signal. However, designing stable artificial neurons and synapse devices with a controllable switching pathway to form a hardware network is a challenge. For the first time, we report that artificial neurons and synapses based on multilayered HfOx/TiOy memristor crossbar arrays can be used for the SNN training of audio signals, which display the tunable threshold switching and memory switching

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25

Ali, Muataz I., and Abbas A. Allawi. "An Artificial Neural Network Prediction Model of GFRP Residual Tensile Strength." Engineering, Technology & Applied Science Research 14, no. 6 (2024): 18277–82. https://doi.org/10.48084/etasr.9107.

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This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks

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26

Zwietering, P. J., E. H. L. Aarts, and J. Wessels. "THE DESIGN AND COMPLEXITY OF EXACT MULTILAYERED PERCEPTRONS." International Journal of Neural Systems 02, no. 03 (1991): 185–99. http://dx.doi.org/10.1142/s0129065791000170.

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We investigate the network complexity of multilayered perceptrons for solving exactly a given problem. We limit our study to the class of combinatorial optimization problems. It is shown how these problems can be reformulated as binary classification problems and how they can be solved by multilayered perceptrons.

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27

Fatin, Syahirah Ab Gani, Khairi Nordin Mohd, Ihsan Mohd Yassin Ahmad, Pasya Ibrahim Idnin, and Syahirul Amin Megat Ali Megat. "Electrocardiogram profiling of myocardial infarction history using MLP and HMLP networks." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 17, no. 1 (2020): 183–90. https://doi.org/10.11591/ijeecs.v17.i1.pp183-190.

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Narrowing of coronary arteries caused by cholesterol deposits deprives heart tissues of oxygen. In prolonged conditions, these will result in myocardium infarction. The presence of damage tissues modifies the normal sinus rhythm and this can be detected using electrocardiogram (ECG). Hence, this paper characterized history of myocardial infarction from survivors using QRS power ratio features from the ECG. Subsequent profiling is performed using multilayered perceptron (MLP) and hybrid multilayered perceptron (HMLP) networks. ECG with history of anterior and inferior infarctions, along with he

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28

Morgan, Nelson, and Hervé Bourlard. "Factoring Networks by a Statistical Method." Neural Computation 4, no. 6 (1992): 835–38. http://dx.doi.org/10.1162/neco.1992.4.6.835.

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We show that it is possible to factor a multilayered classification network with a large output layer into a number of smaller networks, where the product of the sizes of the output layers equals the size of the original output layer. No assumptions of statistical independence are required.

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29

i Shakibian, Hadi, and Nasrollah Moghadam Charkari. "A Multilayered Complex Network Model for Image Retrieval." International Journal of Information and Communication Technology Research 13, no. 4 (2021): 36–42. http://dx.doi.org/10.52547/itrc.13.4.36.

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30

Kouno, Toshiyoshi, Seiichi Motooka, and Nobuyuki Takano. "Water Bottom Material Discrimination Using Multilayered Neural Network." Japanese Journal of Applied Physics 33, Part 1, No. 5B (1994): 3282–85. http://dx.doi.org/10.1143/jjap.33.3282.

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31

Court, M. "Achieving Network Availability in a Multilayered Protocol Environment." IEEE Journal on Selected Areas in Communications 4, no. 7 (1986): 1149–54. http://dx.doi.org/10.1109/jsac.1986.1146415.

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32

Hansel, D., G. Mato, and C. Meunier. "Memorization Without Generalization in a Multilayered Neural Network." Europhysics Letters (EPL) 20, no. 5 (1992): 471–76. http://dx.doi.org/10.1209/0295-5075/20/5/015.

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33

Sakata, Kanji, and Toyoki Kunitake. "A multilayered film of an ultrathin siloxane network." Journal of the Chemical Society, Chemical Communications, no. 6 (1990): 504. http://dx.doi.org/10.1039/c39900000504.

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34

Song, Q., E. K. Teoh, and D. P. Mital. "Multilayered neural network implementation on transputer systolic array." Microprocessing and Microprogramming 41, no. 4 (1995): 289–99. http://dx.doi.org/10.1016/0165-6074(95)00010-l.

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35

Li, Yiqing, Yuqing Li, Xiaoying Gan, Jingchao Wang, Youyun Xu, and Xinbing Wang. "Markov approximation for multilayered selection in satellite network." Journal of Communications and Information Networks 1, no. 3 (2016): 23–31. http://dx.doi.org/10.1007/bf03391567.

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36

Compain, Jean-Daniel, Koji Nakabayashi, and Shin-ichi Ohkoshi. "ChemInform Abstract: A Polyoxometalate-Cyanometalate Multilayered Coordination Network." ChemInform 43, no. 29 (2012): no. http://dx.doi.org/10.1002/chin.201229018.

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37

Bodyanskiy, Yevgeniy, and Artem Dolotov. "A Multilayered Self-Learning Spiking Neural Network and its Learning Algorithm Based on ‘Winner-Takes-More’ Rule in Hierarchical Clustering." Scientific Journal of Riga Technical University. Computer Sciences 40, no. 1 (2009): 66–74. http://dx.doi.org/10.2478/v10143-010-0009-7.

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A Multilayered Self-Learning Spiking Neural Network and its Learning Algorithm Based on ‘Winner-Takes-More’ Rule in Hierarchical ClusteringThis paper introduces architecture of multilayered selflearning spiking neural network for hierarchical data clustering. It consists of the layer of population coding and several layers of spiking neurons. Contrary to originally suggested multilayered spiking neural network, the proposed one does not require a separate learning algorithm for lateral connections. Irregular clusters detecting capability is achieved by improving the temporal Hebbian learning a

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38

Sousa, Cristiana F. V., Catarina A. Saraiva, Tiago R. Correia, et al. "Bioinstructive Layer-by-Layer-Coated Customizable 3D Printed Perfusable Microchannels Embedded in Photocrosslinkable Hydrogels for Vascular Tissue Engineering." Biomolecules 11, no. 6 (2021): 863. http://dx.doi.org/10.3390/biom11060863.

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The development of complex and large 3D vascularized tissue constructs remains the major goal of tissue engineering and regenerative medicine (TERM). To date, several strategies have been proposed to build functional and perfusable vascular networks in 3D tissue-engineered constructs to ensure the long-term cell survival and the functionality of the assembled tissues after implantation. However, none of them have been entirely successful in attaining a fully functional vascular network. Herein, we report an alternative approach to bioengineer 3D vascularized constructs by embedding bioinstruct

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39

Zou, Xiaotao, Bir Bhanu, and Amit Roy-Chowdhury. "Continuous Learning of a Multilayered Network Topology in a Video Camera Network." EURASIP Journal on Image and Video Processing 2009 (2009): 1–19. http://dx.doi.org/10.1155/2009/460689.

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40

Honti, Gergely, and János Abonyi. "Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF Databases." Mathematics 9, no. 4 (2021): 450. http://dx.doi.org/10.3390/math9040450.

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Triplestores or resource description framework (RDF) stores are purpose-built databases used to organise, store and share data with context. Knowledge extraction from a large amount of interconnected data requires effective tools and methods to address the complexity and the underlying structure of semantic information. We propose a method that generates an interpretable multilayered network from an RDF database. The method utilises frequent itemset mining (FIM) of the subjects, predicates and the objects of the RDF data, and automatically extracts informative subsets of the database for the a

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41

Rakshit, Sarbendu, Bidesh K. Bera, Jürgen Kurths, and Dibakar Ghosh. "Enhancing synchrony in multiplex network due to rewiring frequency." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 475, no. 2230 (2019): 20190460. http://dx.doi.org/10.1098/rspa.2019.0460.

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Most of the previous studies on synchrony in multiplex networks have been investigated using different types of intralayer network architectures which are either static or temporal. Effect of a temporal layer on intralayer synchrony in a multilayered network still remains elusive. In this paper, we discuss intralayer synchrony in a multiplex network consisting of static and temporal layers and how a temporal layer influences other static layers to enhance synchrony simultaneously. We analytically derive local stability conditions for intralayer synchrony based on the master stability function

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42

Musial, Katarzyna, Piotr Bródka, Przemysław Kazienko, and Jarosław Gaworecki. "Extraction of Multilayered Social Networks from Activity Data." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/359868.

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The data gathered in all kinds of web-based systems, which enable users to interact with each other, provides an opportunity to extract social networks that consist of people and relationships between them. The emerging structures are very complex due to the number and type of discovered connections. In web-based systems, the characteristic element of each interaction between users is that there is always an object that serves as a communication medium. This can be, for example, an e-mail sent from one user to another or post at the forum authored by one user and commented on by others. Based

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Cao, Xin, Chenyi Wang, and Qiangming Cai. "A Novel Autoregressive Spectral Estimation-Based Neural Network Crest Factor Reduction Structure in Power Amplifiers of 5G Systems in Sandstorm Environment." Scientific Programming 2022 (May 10, 2022): 1–9. http://dx.doi.org/10.1155/2022/8146098.

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In this paper, an autoregressive spectral estimation using a multilayered neural network is proposed to reduce the nonlinearities of 5G MIMO power amplifiers and increase the signal-transmitting qualities. The proposed method adopts the two-sided doubly exponential lattice algorithm to achieve the most suitable estimation for in-band compensation. Also, based on the iterative learning control method, a novel crest factor reduction digital predistortion is combined with the multilayered neural network. Based on the results, the proposed algorithm has increased the linearity and stability of the

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44

Gao, Hongyan, Karen Xiaohe Xu, Bin Chen, Li-Zhu Wu, Chen-Ho Tung, and Hai-Feng Ji. "Ultrahydrophobicity of Polydimethylsiloxanes-Based Multilayered Thin Films." Journal of Nanotechnology 2009 (2009): 1–8. http://dx.doi.org/10.1155/2009/709748.

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The formation of polydimethylsiloxanes (PDMSs)-based layer-by-layer multilayer ultrathin films on charged surfaces prepared from water and phosphate buffer solutions has been investigated. The multilayer films prepared under these conditions showed different surface roughness. Nanoscale islands and network structures were observed homogeneously on the multilayer film prepared from pure water solutions, which is attributing to the ultrahydrobic property of the multilayer film. The formation of nanoscale islands and network structures was due to the aggregation of PDMS-based polyelectrolytes in

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45

Serra-Ricart, M. "Faint Object Classification Using Artificial Neural Networks." Symposium - International Astronomical Union 161 (1994): 249–52. http://dx.doi.org/10.1017/s0074180900047409.

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Artificial Neural Network techniques are applied to the classification of faint objects, detected in digital astronomical images, and a Bayesian classifier (the neural network classifier, NNC hereafter) is proposed. This classifier can be implemented using a feedforward multilayered neural network trained by the back-propagation procedure (Werbos 1974).

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Akashi, Naoto, Mana Toma, and Kotaro Kajikawa. "Design by neural network of concentric multilayered cylindrical metamaterials." Applied Physics Express 13, no. 4 (2020): 042003. http://dx.doi.org/10.35848/1882-0786/ab7cf1.

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47

Mhaskar, H. N. "Approximation properties of a multilayered feedforward artificial neural network." Advances in Computational Mathematics 1, no. 1 (1993): 61–80. http://dx.doi.org/10.1007/bf02070821.

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48

Feinstein, Zachary. "Obligations with Physical Delivery in a Multilayered Financial Network." SIAM Journal on Financial Mathematics 10, no. 4 (2019): 877–906. http://dx.doi.org/10.1137/18m1194729.

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Yee Ann, Lee, P. Ehkan, M. Y. Mashor, and S. M. Sharun. "FPGA-based architecture of hybrid multilayered perceptron neural network." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 2 (2019): 949. http://dx.doi.org/10.11591/ijeecs.v14.i2.pp949-956.

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<span lang="EN-MY">The HMLP is an ANN similar to the MLP, but with extra weighted connections that connect the input nodes directly to the output nodes. The architecture of the HMLP neural network for implementation on FPGA is proposed. The HMLP architecture is designed to be concurrent to demonstrate the parallel nature of the HMLP where each hidden or output node within the same hidden or output layer of the HMLP can calculate its output independently. The HMLP architecture is designed to be modular as well, such that if modification to a module is necessary, only the specific module n

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50

Thiruthuvanathan, Michael Moses, Boppuru Rudra Prathap, Kukatlapalli Pradeep Kumar, Hari Murthy, and Vinay Jha Pillai. "Forecasting Flight Delays with a Multilayered Memory Fusion Network." Procedia Computer Science 258 (2025): 1302–15. https://doi.org/10.1016/j.procs.2025.04.364.

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