Evaluation Algorithm of TV Program Host Performance Based on Emotion Recognition
Main Article Content
Abstract
With the rapid development of information technology and system norms and the incongruity of personnel training speed, the development of news media reports has caused a large number of negative effects. From the current situation of the development of news media reports, due to the lack of perfect management, the difference in media literacy between users and enterprises, entertainment to death, rampant consumer culture, and many other factors, the network media reports are chaotic. In the proposed model Fuzzy Fredholm Integral Market Efficiency (FFI-ME) for the media coverage. With the FFI-ME model, the information technology computes the news media data for the estimation of the coverage to achieve market efficiency. The FFI-ME model uses the Fredholm Integral model to compute the market efficiency for the media data in China. The FFI-ME model computes the news data in China and clusters the data for the classification and detection of the instances in the equation. Through the Integral Fredholm model, the features of the news are estimated to compute the media coverage and efficiency of the news media data. The model uses the Deep learning model for the classification of the data instance in the media data. The simulation analysis expressed that the proposed FFI-ME model achieves a higher classification data accuracy of the 98%.
Article Details
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
References
Dimov, I., Maire, S., & Todorov, V. (2022). An unbiased Monte Carlo method to solve linear Volterra equations of the second kind. Neural Computing and Applications, 1-14.
Nwaigwe, C., Weli, A., & Thanh, D. N. H. (2023). Sixth-Order Numerical Solver Based on Truncation Error for Solution of Nonlinear Fredholm Equations.
Ferragina, P., Lillo, F., & Vinciguerra, G. (2021). On the performance of learned data structures. Theoretical Computer Science, 871, 107-120.
Aihua, G., Yihan, X., & Suzuki, K. (2023). A new MPPT design using ISFLA algorithm and FLC to tune the member functions under different environmental conditions. Soft Computing, 27(3), 1511-1531.
Tan, Z., & Albarakati, A. (2021). Application of Sobolev-Volterra projection and finite element numerical analysis of integral differential equations in modern art design. Applied Mathematics and Nonlinear Sciences, 7(2), 139-150.
González-Rodelas, P., Pasadas, M., Kouibia, A., & Mustafa, B. (2022). Numerical Solution of Linear Volterra Integral Equation Systems of Second Kind by Radial Basis Functions. Mathematics, 10(2), 223.
Díaz, G. A., Mombello, E. E., Pérez, J. J., & Pinzón, D. F. (2021). New method for fast coupled magnetic field-circuit simulation of power transformers based on a semi-analytical approach. International Journal of Electrical Power & Energy Systems, 131, 106976.
Gao, W. (2022). Modeling stock market using new hybrid intelligent method based on MFNN and IBHA. Soft Computing, 26(15), 7317-7337.
Vasileiou, E. (2022). Behavioral finance and market efficiency in the time of the COVID-19 pandemic: does fear drive the market?. In The Political Economy of Covid-19 (pp. 116-133). Routledge.
Wong, J. B., & Zhang, Q. (2022). Stock market reactions to adverse ESG disclosure via media channels. The British Accounting Review, 54(1), 101045.
Li, W., Chien, F., Waqas Kamran, H., Aldeehani, T. M., Sadiq, M., Nguyen, V. C., & Taghizadeh-Hesary, F. (2022). The nexus between COVID-19 fear and stock market volatility. Economic research-Ekonomska istraživanja, 35(1), 1765-1785.
Williamson, B. (2021). Making markets through digital platforms: Pearson, edu-business, and the (e) valuation of higher education. Critical Studies in Education, 62(1), 50-66.
Duz Tan, S., & Tas, O. (2021). Social media sentiment in international stock returns and trading activity. Journal of Behavioral Finance, 22(2), 221-234.
Bofinger, Y., Heyden, K. J., & Rock, B. (2022). Corporate social responsibility and market efficiency: Evidence from ESG and misvaluation measures. Journal of Banking & Finance, 134, 106322.
Bara, A., Affandi, F., Farid, A. S., & Marzuki, D. I. (2021). The Effectiveness of Advertising Marketing in Print Media during the Covid 19 Pandemic in the Mandailing Natal Region. Budapest International Research and Critics Institute-Journal (BIRCI-Journal) Vol, 4(1), 879-886.
Huynh, T. D., & Xia, Y. (2021). Climate change news risk and corporate bond returns. Journal of Financial and Quantitative Analysis, 56(6), 1985-2009.
Wong, J. B., & Zhang, Q. (2022). Stock market reactions to adverse ESG disclosure via media channels. The British Accounting Review, 54(1), 101045.
Sharma, M. K. (2021). An Automated Ensemble-Based Classification Model for The Early Diagnosis of The Cancer Using a Machine Learning Approach. Machine Learning Applications in Engineering Education and Management, 1(1), 01–06. Retrieved from http://yashikajournals.com/index.php/mlaeem/article/view/1
Cepoi, C. O. (2020). Asymmetric dependence between stock market returns and news during COVID-19 financial turmoil. Finance research letters, 36, 101658.
Namejs Zeltins. (2022). Implementation of Shutdown Mode for the Boosting and Inverting Buck-Boost Converter. Acta Energetica, (03), 15–21. Retrieved from https://www.actaenergetica.org/index.php/journal/article/view/472
Somya Lavania, & Bharat Bhushan Jain. (2022). Numerical Simulation and Analysis of Solar Energy Forecasting Using Machine Learning Methodologies. International Journal on Recent Technologies in Mechanical and Electrical Engineering, 9(1), 08–19. https://doi.org/10.17762/ijrmee.v9i1.359
Weimar, D., Holthoff, L. C., & Biscaia, R. (2022). When sponsorship causes anger: Understanding negative fan reactions to postings on sports clubs’ online social media channels. European Sport Management Quarterly, 22(3), 335-357.
Wu, N., Xiao, W., Liu, W., & Zhang, Z. (2022). Corporate climate risk and stock market reaction to performance briefings in China. Environmental Science and Pollution Research, 29(35), 53801-53820.
Brans, H., & Scholtens, B. (2020). Under his thumb the effect of president Donald Trump’s Twitter messages on the US stock market. PloS one, 15(3), e0229931.
Mulhern, F. (2013). Integrated marketing communications: From media channels to digital connectivity. In The evolution of integrated marketing communications (pp. 11-27). Routledge.
Kowalewski, O., & Śpiewanowski, P. (2020). Stock market response to potash mine disasters. Journal of Commodity Markets, 20, 100124.
Nasab, M. A., Mehrazeen, A., & Abadi, A. M. (2022). The Tone of Market Participants' Opinions via Social Media and Capital Market Reaction. Iranian Journal of Accounting, Auditing & Finance (IJAAF), 6(4).
TAN, Ö. F. (2021). The effect of pandemic news on stock market returns during the Covid-19 crash: Evidence from international markets. Connectist: Istanbul University Journal of Communication Sciences, (60), 217-240.
Amirinasab, M., Mehrazeen, A., & Masih Abadi, A. (2022). The Tone of Market Participants' Opinions via Social Media and Capital Market Reaction. Iranian Journal of Accounting, Auditing and Finance, 6(4), 45-60.