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SummaryIn order to improve the security and reliability of computer network, especially the three aspects of fault network diagnosis overall model, computer network link fault detection and computer network congestion prediction, we propose a fuzzy neural network identification and prediction model. In addition, we use rough artificial neural network (RANN) network link mixture to manage all subnets in the network for unified remote allocation network, so as to improve the quality and robustness of the network. The results show that the RANN learning and recognition algorithm we introduced achieves the best performance with 85.12% reduction in total time and 86.82% reduction in the number of steps. In addition, the average error rate of RANN is reduced by 45.94% compared with Lagrangian neural storage algorithm. The iteration number of fuzzy neural network (FNN) neural network prediction model based on incremental rule extraction algorithm is reduced by 28.93%, and the packet loss rate of network peak shaving module controlled by FNN prediction model is reduced to 0.272%, which is 87.69% lower than that of random early detection algorithm. This is expected to provide better performance for the orderly control of network failure, data flow and network data transmission with higher load in the future, and further improve the security and reliability of computer networks.
SummaryIn order to improve the security and reliability of computer network, especially the three aspects of fault network diagnosis overall model, computer network link fault detection and computer network congestion prediction, we propose a fuzzy neural network identification and prediction model. In addition, we use rough artificial neural network (RANN) network link mixture to manage all subnets in the network for unified remote allocation network, so as to improve the quality and robustness of the network. The results show that the RANN learning and recognition algorithm we introduced achieves the best performance with 85.12% reduction in total time and 86.82% reduction in the number of steps. In addition, the average error rate of RANN is reduced by 45.94% compared with Lagrangian neural storage algorithm. The iteration number of fuzzy neural network (FNN) neural network prediction model based on incremental rule extraction algorithm is reduced by 28.93%, and the packet loss rate of network peak shaving module controlled by FNN prediction model is reduced to 0.272%, which is 87.69% lower than that of random early detection algorithm. This is expected to provide better performance for the orderly control of network failure, data flow and network data transmission with higher load in the future, and further improve the security and reliability of computer networks.
PurposeThe aim of this paper is to develop a theoretical framework of the transformation of the bank's scope driven by fintechs.Design/methodology/approachThe conceptual foundations for a comprehensive transformation of the bank governance through financial technologies (fintechs) are underexplored. In order to develop such foundations, the authors adopt transaction cost economics (TCE), the concepts of external enablers and a modular organizational design, as well as a systematic literature review.FindingsThe results point to three scenarios of the banks' scope, depending on the adopted technological mechanisms and related effects that change the characteristics of organizational activities, justifying new bank boundaries. The most advanced application of fintechs results in a modularized network scenario leading to the emergence of financial ecosystems.Research limitations/implicationsThe proposed micro-perspective of decisional rules in an individual organization is unique in the current literature that predominantly focuses on the banking sector at large. The identified scenarios are valuable for solid theoretical and empirical grounding and can be further exploited in decision simulations and empirical studies.Practical implicationsThe proposed theoretical framework points to the rationales and consequences of adopted technologies for the boundaries of a bank organization.Originality/valueThis paper provides three contributions to the literature on technology-driven transformations of organizations with a focus on banks. First, the authors elaborate a theoretical framework for establishing the bank's boundaries in response to the expansion of financial technologies. Second, the authors add to the knowledge accumulation in the area of organizational transformations based on the ICT adoption, in particular, to the literature on the modular organizational design. Third, the authors contribute to the decision-maker practice by proposing the alternative options of banks' scope transformed through fintechs.
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