An Innovative Career Management Platform Empowered by AI, Big Data, and Blockchain Technologies: Focusing on Female Engineers

Jang, Ji-Young; Kyun, Suna

doi:10.14704/web/v19i1/web19284

Cited by 6 publications

(3 citation statements)

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“…e infrastructure construction process is often accompanied by the redistribution of many resource factors, such as capital, talent, and technology [22][23][24][25]. Infrastructure connectivity can promote trade and investment facilitation and reduce the cost of factor flow [26][27][28]. Some scholars believe that intellectual property protection is an important factor that affects the innovation capacity of service industries and improving the level of intellectual property protection is conducive to the technological innovation of service industries [29,30].…”

Section: Literature Reviewmentioning

confidence: 99%

Creating Sustainable Cultural Industries: The Perspective of Artificial Intelligence and Global Value Chain

Liu

Song

2022

Journal of Environmental and Public Health

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In the era of artificial intelligence (AI), cultural industries have introduced new development opportunities, and their global value chain (GVC) position is receiving more attention. This study uses panel data from global cross-borders from 56 countries (regions) as the research sample to empirically analyze the impact of AI on improving the GVC position of cultural industries using the double fixed effects regression model and examines the heterogeneity effect. The results confirm that there is a significant positive correlation between AI and the GVC position of cultural industries. The mechanism test shows that AI impacts the division of labor position in the GVC of cultural industries mainly through technological innovation and the industrial structure. Heterogeneity analysis shows that AI has a significant effect on promoting the cultural industry’s GVC position in high-income countries (regions) but it has no significant effect on low- and middle-income countries (regions). The results of this study can provide a useful reference for improving the division of labor positions in the GVC and better promoting the development of cultural industries.

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Section: Literature Reviewmentioning

confidence: 99%

Creating Sustainable Cultural Industries: The Perspective of Artificial Intelligence and Global Value Chain

Liu

Song

2022

Journal of Environmental and Public Health

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“…ere are 88 training samples for 88 monophonic sounds. In this paper, 88 inputs are used as the centroid set C, and the function radius is calculated according to the following formula to determine the functional form of each node in the hidden layer of music appreciation [25].…”

Section: Music Appreciation Quality Optimization Based On Kalman Filt...mentioning

confidence: 99%

Digital Development Path of Music Appreciation Based on the Kalman Filter

2022

Mathematical Problems in Engineering

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For a long time, due to the influence of curriculum orientation and examination-oriented education, the learning of music appreciation courses has not been paid much attention. This makes the teaching method in music appreciation teaching single, the classroom effect cannot reach the expected learning goal, and the classroom teaching efficiency becomes low, which urgently needs a new learning method to deal with these problems. Focusing on the digital education background of the new curriculum reform, this paper investigates the teaching nature, teaching methods, teaching design and evaluation, classroom construction, and student ability development of music appreciation. In this paper, the Kalman filter algorithm is used, and the optimal transformation order determined in advance is used to suppress the noise in music and improve the sound quality of music appreciation. If the order of the full-band signal model is selected as 10, and the number of sub-bands is selected as 4, then the order of the sub-band signal model should be at least greater than or equal to 2. In the music appreciation teaching test based on the Kalman filter, it was found that 35.6% of the students believed that the music appreciation course based on the Kalman filter could completely improve the learning efficiency, and 49% of students believe that the teaching of music appreciation class based on the Kalman filter can improve a large part of the learning efficiency. Overall, 85.5% of the students believed that the music appreciation class based on the Kalman filter is of great help in improving the learning efficiency, and the Kalman filter denoising method proposed in this paper has an obvious effect, which is a new attempt to promote the digital development of music appreciation.

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“…With the rapid development of cloud computing, Internet of Things, Internet and other technologies and Web 2.0, in many scenarios such as the network and digital libraries [ 1 – 3 ], a large amount of information data is constantly emerging from book resources, which increases people's access to the required book information difficulty, resulting in ‘information overload [ 4 , 5 ]. How to solve the ‘information overload' and help users quickly find the book resources that meet their individual demand is an urgent problem to be solved [ 6 , 7 ], because book recommendation algorithms can realize ‘one-to-one' service according to users' personal preferences, to provide users with personalized services book recommendations.…”

Section: Introductionmentioning

confidence: 99%

Research on Personalized Book Recommendation Based on Improved Similarity Calculation and Data Filling Collaborative Filtering Algorithm

Peng

Dou

et al. 2022

Computational Intelligence and Neuroscience

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(Purpose/Significance). This paper aims at the problems of inaccurate recommendation effect caused by data sparseness and cold start in the traditional collaborative filtering-based book personalized recommendation algorithm. So this paper proposes a collaborative filtering recommendation algorithm which improves the similarity solution method and the filling method of missing data. (Method/Process). By considering the influence of the user’s common rating book collection on the similarity calculation, the average rating value of all books is used as the threshold, and the user’s common rating weight is introduced into the user’s similarity calculation. As for data filling, according to the user’s average rating, the basic attributes such as the age and gender of users are coded, and then Euclidean distance is initially calculated, making hierarchical clustering on users. What’s more, Shope-one algorithm is used to calculate the filling value of the former m similar users，and add the weight value of the degree simultaneously to get the final filling value, so as to improve the data filling method. (Result/Conclusion). Experiments were carried out with the data set of Book-Crossing Data set through Python. The experimental results show that the improved collaborative filtering algorithm has a significantly improvement in the accuracy and quality of book recommendation.

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An Innovative Career Management Platform Empowered by AI, Big Data, and Blockchain Technologies: Focusing on Female Engineers

Cited by 6 publications

References 0 publications

Creating Sustainable Cultural Industries: The Perspective of Artificial Intelligence and Global Value Chain

Creating Sustainable Cultural Industries: The Perspective of Artificial Intelligence and Global Value Chain

Digital Development Path of Music Appreciation Based on the Kalman Filter

Research on Personalized Book Recommendation Based on Improved Similarity Calculation and Data Filling Collaborative Filtering Algorithm

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