Risk assessment of knowledge fusion in an innovation ecosystem based on a GA-BP neural network

Wang, Lin; Bi, Xinhua

doi:10.1016/j.cogsys.2020.12.006

Cited by 47 publications

(20 citation statements)

References 4 publications

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“…This greatly improves the accuracy of prediction and evaluation, but a review of the relevant literature shows that the analysis of the importance of the impact of indexes is often neglected. Thus, in this paper, based on the consideration of machine learning, according to the different degrees of risk impact, AHP method is used to determine the index weight, which overcomes the deficiency of subjective consideration in previous studies [ 15 ]; the genetic algorithm optimized BP neural network (hereinafter referred to as GABP) with better prediction and evaluation effect is used for evaluation [ 16 ], which is a successful attempt to realize the combination of energy field and deep learning. In addition, for the security and privacy risk assessment of energy big data, the current literature pays more attention to theoretical analysis and lacks a relatively perfect assessment reference system.…”

Section: Introductionmentioning

confidence: 99%

Security and Privacy Risk Assessment of Energy Big Data in Cloud Environment

Shi

et al. 2021

Computational Intelligence and Neuroscience

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Considering the importance of energy in our lives and its impact on other critical infrastructures, this paper starts from the whole life cycle of big data and divides the security and privacy risk factors of energy big data into five stages: data collection, data transmission, data storage, data use, and data destruction. Integrating into the consideration of cloud environment, this paper fully analyzes the risk factors of each stage and establishes a risk assessment index system for the security and privacy of energy big data. According to the different degrees of risk impact, AHP method is used to give indexes weights, genetic algorithm is used to optimize the initial weights and thresholds of BP neural network, and then the optimized weights and thresholds are given to BP neural network, and the evaluation samples in the database are used to train it. Then, the trained model is used to evaluate a case to verify the applicability of the model.

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Section: Introductionmentioning

confidence: 99%

Security and Privacy Risk Assessment of Energy Big Data in Cloud Environment

Shi

et al. 2021

Computational Intelligence and Neuroscience

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“…This group, in our opinion, can also be attributed to the trust manifested in inter-firm and multi-level cooperation and investments of all interested parties [63][64][65]. It should be noted that, according to many authors, trust in global value chains is closely related to the competitiveness of companies through joint innovation, training and knowledge exchange aimed at reducing uncertainty and risks in the field of interaction of partners within the chain [66][67][68].…”

Section: Factors Influencing the Implementation Of Innovation In Global Value Chainsmentioning

confidence: 79%

Strategic Innovation as a Factor of Adaptation of National Economies to the Development of Global Value Chains

et al. 2021

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In age of sustainable development, strategic innovations have become the most important factor in the adaptation of national economies to dynamic global changes, encompassing trade and economic relations between the leading and developing countries of the World. At the same time, the task of this study was to reveal the complex and contradictory role of strategic innovations in the development of national economies against the background of the transformation of global value chains (GVCs). Main methods for solving the problem were empirical methods of comparative and structural analysis, as well as econometrics. The study analyzed 44 countries classified by the World Bank in the group of countries with per capita incomes below and above the average, as well as with high income. Results of the calculations made it possible to establish a highly differentiated relationship between the share of products manufactured by foreign companies operating in the host countries, on the one hand, and indicators of the dynamics of foreign direct investment (the number of researchers engaged in R&D, the number of technical specialists involved in research and development (R&D), the cost of research and development in the territory of the host countries), on the other hand. This made it possible to determine the role of strategic innovation in the adaptation of national economies. The established dependencies expand the understanding of the role of strategic innovations in the formation and further development of global value chains and their significance in evolution: from process and product innovations of individual companies to the formation of global innovation ecosystems.

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“…The BP neural network used in this study is a special machine learning method which focuses on intelligent predictions rather than improvements of computer algorithms, therefore the required amount of training data can be less than the traditional machine learning techniques, and a few studies have shown that training data with a size around 20 can yield acceptable training results [ 70 , 71 , 72 , 73 , 74 ]. The present study performed 28 cyclic triaxial tests on the UGM.…”

Section: Evaluation Of Critical Dynamic Stress and Final Accumulative Plastic Strainmentioning

confidence: 99%

Evaluation of Critical Dynamic Stress and Accumulative Plastic Strain of an Unbound Granular Material Based on Cyclic Triaxial Tests

et al. 2021

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Critical dynamic stress (σcri) and accumulative plastic strain (εp) are primary indicators regarding the dynamic stability of unbound granular materials (UGMs). This study aims to seek an effective method to evaluate the dynamic stability of UGMs used in railway subgrades. First, the dynamic characteristics of an UGM used in railway subgrade bed construction were investigated by performing a series of large-scale cyclic triaxial tests, with the results showing that εp versus cycle number (N) curves can be categorized into stable, failure, and critical patterns. Grey relational analyses were then established, where the analyzed results demonstrated that the εp–N curve pattern and final accumulative plastic strain (εs) of the stable curves are strongly correlated with the moisture content (w), confining pressure (σ3), and dynamic deviator stress (σd). The analyzed grey relational grades distributed in a narrow range of 0.72 to 0.81, indicating that w, σ3, and σd have similar degrees of importance on determining the εp–N curve patterns and the values of εs of the UGM. Finally, a data processing method using a back-propagation (BP) neural network is introduced to analyze the test data, and an empirical approach is developed to evaluate the σcri (considering the effects of σ3 and w) and εs (considering the effects of σ3, w, and σd) of the UGM. The analyzed results illustrated that the developed method can effectively reflect the linear/non-linear relationships of σcri and εs with respect to σ3 and/or σd. The σcri approximately increases linearly with increasing σ3, and a simple empirical formula is proposed for the σcri. In addition, εs and its variation rate increase non-linearly with increasing σd but decrease non-linearly as σ3 increases.

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Risk assessment of knowledge fusion in an innovation ecosystem based on a GA-BP neural network

Cited by 47 publications

References 4 publications

Security and Privacy Risk Assessment of Energy Big Data in Cloud Environment

Security and Privacy Risk Assessment of Energy Big Data in Cloud Environment

Strategic Innovation as a Factor of Adaptation of National Economies to the Development of Global Value Chains

Evaluation of Critical Dynamic Stress and Accumulative Plastic Strain of an Unbound Granular Material Based on Cyclic Triaxial Tests

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