scholarly journals Research and analysis of music development based on k-means and PCA algorithm

2021 ◽  
Vol 2083 (3) ◽  
pp. 032044
Author(s):  
Zimo Cai ◽  
Luqi Fu ◽  
Wenchao Li
Keyword(s):  
Interaction Strength ◽  
Cosine Similarity ◽  
Directed Network ◽  
Analysis Model ◽  
Music Similarity ◽  
Advantages And Disadvantages ◽  
Model Based ◽  
Network Algorithm ◽  
Pca Algorithm ◽  
Over Time

Abstract The purpose of this article is to establish an algorithm model that can measure the influence of music, capture the evaluation index reflecting the influence of music, and extend the model to other fields such as politics, culture, and society. We have established a music influence-oriented network algorithm model based on influencers and followers, where each artist is a node, and each follower is a connection between artists. We define relative interaction strength indicators to help understand the entire network algorithm. In addition, we also used time, genre and other scales to further optimize the network algorithm. We first use the PCA algorithm to determine indicators that reflect music similarity, such as vitality, activity, popularity, overall loudness, etc. On this basis, an evaluation algorithm model based on cosine similarity is established to calculate music similarity values of different genres. In addition, we use the K-MEANS algorithm to normalize each feature index and sum its variance. Finally, we noticed that the similarity of artists within genres is higher than the similarity of artists between genres. We further analyzed the differences and influences within and between genres. Taking time as a distinction, a relative heat map of the interactive influence of genres is drawn. It is understood that certain genres will obviously have a certain influence over time. We summarize this model as an impact correlation analysis model. First, we choose a representative influencer. Then, based on the cosine similarity, we obtained the music similarity with the fans in batches, thus more intuitively concluded that the Internet celebrities did affect the respective artists. In addition, we combined the calculation of SPSS variance and selected different indicators to visualize the radar chart to understand the attractiveness differences of certain music features. We first select the musical characteristics with obvious changing trends, then locate the position of the changer in the music evolution process through the time distribution diagram of the corresponding work, and finally select the representative changer. We analyzed the change history of each indicator in the selected genre over time, and finally got the global directed network diagram. Based on the network algorithm model established in the previous question, we analyzed the background of the times and found that there is an interaction between music and the cultural environment. Finally, we also analyzed the advantages and disadvantages of the algorithm model, and discussed the application of the method in other fields.

scholarly journals Cosine similarity cluster analysis model based effective power systems fault identification

2017 ◽  
Vol 4 (1) ◽  
pp. 123-130 ◽  
Author(s):  
Tan Yong Sing ◽  
◽  
Syahrel Emran Bin. Siraj ◽  
Raman Raguraman ◽  
Pratap Nair Marimuthu ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Power Systems ◽  
Cosine Similarity ◽  
Fault Identification ◽  
Analysis Model ◽  
Effective Power ◽  
Model Based

scholarly journals A Survey of Stakeholder Visualization Approaches

2016 ◽  
Vol 2 (1,2) ◽  
Author(s):  
Martin Cenek ◽  
Ondřej Částek
Keyword(s):  
Three Dimensional ◽  
The Other ◽  
Dimensional Model ◽  
Three Dimensional Model ◽  
Advantages And Disadvantages ◽  
The Third ◽  
Model Based ◽  
The Relationship ◽  
Over Time

The aim of this paper is to present an overview of studies for the representation/visualization of stakeholders with a proposal of our own method of visualization. The following text examines the existing representational methods and at the same time critically evaluates their advantages and disadvantages. In addition, our own proposed approach is also presented.The need to develop visualization methods for use in the concept of stakeholders has been accepted by researchers, and it is possible to encounter number of various alternatives which have been applied more or less successfully. The shared weakness of the majority of the models is that they only represent two main attributes simultaneously. When such models do contain three variables, then the third one is only a complementary aspect of the relationship compared to the two dominant attributes.Our proposed visualisation model based on three Mitchell´s (1997) stakeholder attributes should overcome the before mentioned disadvantage. Also, it takes into account the development over time in accordance with the dynamic of the relationships with the stakeholders. Therefore, the proposed three-dimensional model meets these needs and simultaneously removes the shortcomings of the other models, which are identified in our overview presented in this paper.

scholarly journals Analysis of Industry Convergence Based on Improved Neural Network

2021 ◽  
Author(s):  
Nan Ma
Keyword(s):  
Neural Network ◽  
Convergence Analysis ◽  
Information Age ◽  
Analysis Model ◽  
Model Based ◽  
Industry Convergence ◽  
Network Algorithm ◽  
The Neural Network ◽  
Group Competition ◽  
Neural Network Algorithm

Abstract Economic growth in the information age is no longer a stage driven by unipolarity. It has entered a multi-polar driving stage characterized by integration, fusion, and integrated development on a larger scale between regions, and the trend of group competition with urban agglomerations as carriers has become increasingly obvious. This paper improves the neural network algorithm based on the needs of industrial economic integration in the digital age, and proposes an industry convergence analysis model based on the improved neural network algorithm. Moreover, this article combines industry models to analyze actual needs and constructs an industry convergence analysis model based on improved neural networks, and analyzes the integration of different industries. In addition, this article conducts experiments through multiple sets of data, and combines the neural network model of this article to conduct research. Through experimental research, we know that the model constructed in this paper can play an important role in the analysis of industry convergence.

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

Regional effect of monetary policy based on GVAR model and machine learning

2020 ◽  
pp. 1-13
Author(s):  
Zengming Zhao ◽  
Wenting Chen
Keyword(s):  
Machine Learning ◽  
Growth Rate ◽  
Monetary Policy ◽  
Stress Test ◽  
Analysis Model ◽  
Conduction Model ◽  
Regional Effect ◽  
Macroeconomic Factors ◽  
Model Based ◽  
Gray Correlation

Monetary policy is an important means for a country to regulate macroeconomic operations and achieve established economic goals. Moreover, a reasonable monetary policy improves the efficiency of financial operations on a global scale and effectively resolves the financial crisis. At present, scholars from various countries have begun to pay attention to the issue of differentiated formulation of monetary policy among regions. This paper combines machine learning to construct a monetary policy differentiation effect analysis model based on the GVAR model. Moreover, this paper uses the gray correlation analysis method to obtain the gray correlation matrix between industries, and then introduces the industry’s own characteristics, industry relevance and macroeconomic factors into the macro stress test of credit risk. In addition, this paper constructs a conduction model based on the industry GVAR model, and uses the first-order difference sequence of GDP growth rate, CPI growth rate and M2 growth rate of each economic region to construct a GVAR model to test the impulse response function. The results of the test show that the monetary policy shocks of various economic regions are significantly different. All in all, the research results show that the performance of the model constructed in this paper is good.

SWOT-ANALYSIS OF OUTSOURCING OF ACCOUNTING SERVICES

2021 ◽  
Vol 296 (4) ◽  
pp. 95-99
Author(s):  
ILONA ADASIUK ◽  
◽  
OKSANA MARTYNIUK ◽  
Keyword(s):  
Strategic Management ◽  
Swot Analysis ◽  
External Environment ◽  
External Factors ◽  
Analysis Model ◽  
Advantages And Disadvantages ◽  
Business Goals ◽  
Potential Risks

The article based on SWOT-analysis of outsourcing of accounting services. Accounting services can be improved if you can work on your strengths, as well as correct the weaknesses of the company or area where you lose points. SWOT-analysis is considered as a tool of strategic management to determine the feasibility of using accounting outsourcing as a way to optimize the enterprise. SWOT consists mainly of two main parts: strengths and weaknesses, which will indicate the internal aspect, and threats and opportunities related to external factors for the company (but those that are present in the environment). In essence, we test the effectiveness of the accounting services provided by the contractors that your business provides against this background. With this analysis, the firm can understand the problems, ie where they lack work and why, because these are periods during the year when work becomes fragile. Why this happens is best analyzed because of it. A set of business goals are things that outsourcing companies need to invest because simple marketing is not enough. The analysis of advantages and disadvantages of accounting outsourcing is carried out and the necessity of SWOT-analysis of potential risks and advantages of using perspectives of accounting outsourcing is emphasized. The internal and external environment was analyzed using an extended SWOT analysis model from the standpoint of strengths and weaknesses, opportunities and expected threats. Based on the results of the SWOT-analysis, a matrix has been formed that will allow predicting threats and potential opportunities at the stage of planning the use or refusal to use accounting outsourcing services.

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