Methodology based on the NIST cybersecurity framework as a proposal for cybersecurity management in government organizations

Delgado, Maurice Frayssinet; Esenarro, Doris; Regalado, Francisco Fernando Juárez; Reátegui, Mónica Díaz

doi:10.17993/3ctic.2021.102.123-141

Cited by 23 publications

(9 citation statements)

References 2 publications

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“…The table shows the evaluation index of the prediction results of the three models on the four factors respectively. It can be seen that W-LSTM has better results than other models, and it shows that the W-LSTM model has good universality [28][29]. It can be seen from the above experiments that the W-LSTM model has higher accuracy, better fitting degree and good universality.…”

Section: Figure 2 W-lstm Prediction Resultsmentioning

confidence: 74%

Research on physical fitness training of football players based on improved LSTM neural network to improve physical energy saving and health

Pan¹

2023

3C Tecnología

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In order to ensure that the physical function of football players adapts to the development of modern football level, and avoid the phenomenon of inability to adapt to the intensity of modern football games due to lack of physical fitness. Aiming at the physical training of football players, this paper proposes an improved long-short-term memory network (W-LSTM) model for the optimization and prediction of physical training. The model effectively combines the global feature extraction ability of LSTM for time series data and the preprocessing ability of the extracted data, which reduces the loss of feature information and achieves high prediction accuracy. The front door is added on the basis of LSTM, which combines training and physical function to reduce the impact of fluctuations in data outliers on the prediction results, effectively improving the accuracy of physical training optimization and prediction, and using body shape, exercise tolerance, exercise intensity and fitness level as input values to conduct comparative experiments on the three models of W-LSTM, LM-BP and ARIMA. The study found that W-LSTM has a lower mean square error (0.011) and a higher correlation coefficient (0.985), indicating that the model proposed in this paper is significantly better than other existing comparison models in terms of the accuracy of prediction results.

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Section: Figure 2 W-lstm Prediction Resultsmentioning

confidence: 74%

Research on physical fitness training of football players based on improved LSTM neural network to improve physical energy saving and health

Pan¹

2023

3C Tecnología

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“…In the practical control of machine learning models, the angular output of each joint is a continuous value, and if the range of values taken for each joint angle is discretized, the number of behaviors grows exponentially with the number of degrees of freedom. If this accuracy is further improved, the number of values taken will grow exponentially [28][29].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Reconstruction of physical dance teaching content and movement recognition based on a machine learning model

Li¹,

Yang²

2023

3C TIC

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With the technological development of movement recognition based on machine learning model algorithms, the content and movements for physical dance teaching are also seeking changes and innovations. In this paper, a set of three-dimensional convolutional neural network recognition algorithms based on a machine learning model is constructed through the collection to recognition of sports dance movement data. By collecting the skeleton information of typical movements of physical dance, a typical movement dataset of physical dance is constructed, which is recognized by the improved 3D convolutional neural network recognition algorithm under the machine learning model, and the method is validated on the public dataset. The experimental results show that the 3D CNNs in this paper can produce relatively satisfactory results for sports dance action recognition with high accuracy of action recognition, which verifies the feasibility of the 3D convolutional neural network action recognition algorithm under the machine learning model for the acquisition to recognition of sports dance actions. It illustrates that the future can be better to open a new direction of physical dance education content through machine learning models in this form.

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“…Land and space resources: Land and space resources are the most traditional resources and the most basic elements that enable education and teaching to take place. It mainly includes natural resources such as suitable teaching land and teaching space [29][30]. However, with the development of the times and the advancement of network technology, its basic role has not changed but its importance is weakening.…”

Section: Table 1 Classification Of Higher Education Resourcesmentioning

confidence: 99%

The Optimization Path of Higher Education Resource Allocation in China Based on Fuzzy Set Theory

Zhao¹

2023

3C TIC

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The use of fuzzy set theory to adjust the educational resource allocation model is the optimal path to achieve the allocation of educational resources. The optimal path realized by fuzzy set theory promotes the healthy development of education and addresses the current important and urgent tasks in the field of higher education in China. This study utilizes the basic theory of fuzzy sets and analyzes dynamic fuzzy set theory, fuzzy relations and fuzzy matrices. The indexes of optimal allocation are selected according to the classification of higher education resources, the fuzzy set multi-objective planning model is designed, and finally, the combination of factors of education production in colleges and universities is optimized. The experiment proves that: the average allocation efficiency of excellent colleges and universities is 0.186, and the average improvement is 35.41% after optimization. The average allocation efficiency of ordinary colleges and universities is 0.174, which is improved by 22.12% on average after optimization. It can be found that the resource allocation efficiency of excellent colleges and universities is generally higher than that of ordinary colleges and universities, and the role of excellent colleges and universities with more abundant resources themselves is greater after the optimization of college resource allocation. It indicates that by adjusting the quantity and structure of educational resources according to the optimization path, the optimization of educational resource allocation can be achieved. This verifies that: the emergence of optimal paths for solving resource allocation realized by fuzzy set theory has greatly expanded the application of fuzzy methods in the field of higher education and provided a new theoretical tool for solving resource allocation.

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Methodology based on the NIST cybersecurity framework as a proposal for cybersecurity management in government organizations

Cited by 23 publications

References 2 publications

Research on physical fitness training of football players based on improved LSTM neural network to improve physical energy saving and health

Research on physical fitness training of football players based on improved LSTM neural network to improve physical energy saving and health

Reconstruction of physical dance teaching content and movement recognition based on a machine learning model

The Optimization Path of Higher Education Resource Allocation in China Based on Fuzzy Set Theory

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