Detection and Localization of Failures in Hybrid Fiber–Coaxial Network Using Big Data Platform

Simakovic, Milan; Cica, Zoran

doi:10.3390/electronics10232906

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…In order to better describe the corresponding relationship between happiness index and film box office, two variables, Q cc (m) [21,22] and degree of freedom m [23,24], are used to study the correlation between happiness index and film box office [25,26]. e logarithmic curve of statistics and degree of freedom is shown in Figure 7.…”

Section: Correlation Analysis Of Happiness Index and Box Officementioning

confidence: 99%

Correlation Analysis of Network Big Data and Film Time-Series Data Based on Machine Learning Algorithm

Li¹,

Xia

2022

Mathematical Problems in Engineering

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To expand the application of machine learning in movie data, in order to explore the correlation between network big data and film time-series data, based on the machine learning algorithm, the correlation and multifractal characteristics of happiness index (HI) and film box office (BO) were studied and described by introducing multifractal crossover method. On this basis, some indicators are introduced to optimize the neural network model so that the optimization model can describe and predict the box office and other related information well. The results show that the critical values of the happiness index and box office show a linear change trend with the increase of freedom, and the corresponding change curves of the happiness index and box office show obvious nonlinear characteristics, which can be divided into slow increase stage, steady increase stage, and approximately gentle stage. With the increase of iteration parameter q value, the change trend of the long-term and short-term curves of the generalized Hurst function is basically the same, and the difference between the two is getting smaller and smaller, while the difference between the two curves is getting bigger and bigger with the increase of q value of Renyi function. The changing trend of the dynamic Hurst index in the sliding window period all shows that it first rises rapidly to a certain value, then fluctuates rapidly with the increase of time, then drops rapidly to a constant value, and finally continues to show repeated small range fluctuation. Under the influence of time-series parameter α, the original sequence changes the most, the replacement sequence changes the medium, and the corresponding rearrangement sequence changes the least. The overall distribution of box office prediction data conforms to the characteristics of linear variation. The prediction index of the optimized HI-LSTM (Happiness Index-Long term short term memory neural network) model is higher in the box office, indicating that the model has better performance in describing and predicting the box office. This study can provide a theoretical basis for the correlation study of network big data and film data.

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Section: Correlation Analysis Of Happiness Index and Box Officementioning

confidence: 99%

Correlation Analysis of Network Big Data and Film Time-Series Data Based on Machine Learning Algorithm

Li¹,

Xia

2022

Mathematical Problems in Engineering

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“…Humanistic quality mainly refers to people's internal quality and people-centered spirit. It can be seen that the current aesthetic education includes diversified education, such as aesthetic appreciation, moral quality, thought and emotion, and personality shaping [18,19]. In addition to classroom teaching, college aesthetic education can also be realized through campus cultural activities such as lectures, community activities, literary and artistic activities, and competitions.…”

Section: Improvement Of College Aestheticmentioning

confidence: 99%

Construction of College Aesthetic Education Quality Improvement Model under Big Data Network

Wang

2022

Wireless Communications and Mobile Computing

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In view of the importance and urgency of college aesthetic education, this paper constructs a quality improvement model of college aesthetic education under the big data network. Firstly, it puts forward the characteristics of planning, step-by-step, scientific, and centralized aesthetic education in colleges and universities and then expounds the importance of aesthetic education in colleges and universities, including the importance of aesthetic education in spiritual education and in the construction of socialist spiritual civilization. Integrating the functions of data mining under the big data network, using the analytic hierarchy process to construct the improvement index, and carrying out the consistency test at the same time, we can get the improvement index system of aesthetic education quality in colleges and universities. The fuzzy judgment matrix is constructed to obtain the final improvement result. The experimental results show that the constructed model verifies the importance and urgency of aesthetic education in colleges and universities, has a high degree of freedom, and reduces the complexity of quality improvement.

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Data Mining Applied to the HFC Network to Analyze the Availability of Telecommunication Services

Alarcón-Loza,

Estacio-Corozo

2024

Lecture Notes in Networks and Systems

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Detection and Localization of Failures in Hybrid Fiber–Coaxial Network Using Big Data Platform

Cited by 5 publications

References 31 publications

Correlation Analysis of Network Big Data and Film Time-Series Data Based on Machine Learning Algorithm

Correlation Analysis of Network Big Data and Film Time-Series Data Based on Machine Learning Algorithm

Construction of College Aesthetic Education Quality Improvement Model under Big Data Network

Data Mining Applied to the HFC Network to Analyze the Availability of Telecommunication Services

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