Assessment of Chinese Cultural Influence and Market Potential in Malaysian Chinese-Language Films Based on Big Data Analysis and Predictive Models
Keywords:
Big Data Analytics, Cultural influences, Market Potential, Predictive models, Stochastic Gradient Descent (SGD), Software Defined Network (SDN)Abstract
The paper presents a comprehensive investigation into the dynamic interplay of Chinese cultural influence and market potential within the context of Malaysian Chinese-language films. Big data analysis and predictive modeling, the study explores various scenarios to unveil the underlying correlations between cultural elements, market opportunities, and box office success. With integrating Stochastic Gradient Descent (SGD) with Software-Defined Networking (SDN), the research enhances data processing accuracy and efficiency, providing a robust framework for decision-making in the film industry. Through a meticulous analysis of scenarios, the study reveals the intricate relationship between cultural impact and market potential, shedding light on how cultural influence contributes to box office revenue. Additionally, an evaluation of data processing aspects offers insights into optimizing computational strategies. This paper's findings offer valuable insights for film industry stakeholders seeking to navigate the intersection of culture, market dynamics, and data-driven decision-making, ultimately advancing the success of Chinese-language films in the Malaysian market. Our findings underscore the pivotal role of cultural impact in shaping market viability, as evidenced by high correlation coefficients (r > 0.97) between Cultural Influence and Market Potential Score. With a voluminous dataset, the study attains a fine-grained understanding of these films, reaffirming the symbiotic relationship between cultural narratives and box office achievements. Moreover, the research evaluates the practical dimensions of data processing, revealing the computational intricacies encapsulated by Processing Time, Memory Usage, and Input Data Size
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Fan, C., Yan, D., Xiao, F., Li, A., An, J., & Kang, X. (2021, February). Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches. In Building Simulation (Vol. 14, pp. 3-24). Tsinghua University Press.
Wang, J., Xu, C., Zhang, J., & Zhong, R. (2022). Big data analytics for intelligent manufacturing systems: A review. Journal of Manufacturing Systems, 62, 738-752.
Fathi, M., Haghi Kashani, M., Jameii, S. M., & Mahdipour, E. (2022). Big data analytics in weather forecasting: A systematic review. Archives of Computational Methods in Engineering, 29(2), 1247-1275.
Sun, Z., & Huo, Y. (2021). The spectrum of big data analytics. Journal of Computer Information Systems, 61(2), 154-162.
Li, L., Lin, J., Ouyang, Y., & Luo, X. R. (2022). Evaluating the impact of big data analytics usage on the decision-making quality of organizations. Technological Forecasting and Social Change, 175, 121355.
Gandomi, A. H., Chen, F., & Abualigah, L. (2022). Machine learning technologies for big data analytics. Electronics, 11(3), 421.
Ahmed, I., Ahmad, M., Jeon, G., & Piccialli, F. (2021). A framework for pandemic prediction using big data analytics. Big Data Research, 25, 100190.
Guo, C., & Chen, J. (2023). Big data analytics in healthcare. In Knowledge Technology and Systems: Toward Establishing Knowledge Systems Science (pp. 27-70). Singapore: Springer Nature Singapore.
Alsunaidi, S. J., Almuhaideb, A. M., Ibrahim, N. M., Shaikh, F. S., Alqudaihi, K. S., Alhaidari, F. A., ... & Alshahrani, M. S. (2021). Applications of big data analytics to control COVID-19 pandemic. Sensors, 21(7), 2282.
Ranjan, J., & Foropon, C. (2021). Big data analytics in building the competitive intelligence of organizations. International Journal of Information Management, 56, 102231.
Maheshwari, S., Gautam, P., & Jaggi, C. K. (2021). Role of Big Data Analytics in supply chain management: current trends and future perspectives. International Journal of Production Research, 59(6), 1875-1900.
Batko, K., & Ślęzak, A. (2022). The use of Big Data Analytics in healthcare. Journal of big Data, 9(1), 3.
Ashaari, M. A., Singh, K. S. D., Abbasi, G. A., Amran, A., & Liebana-Cabanillas, F. J. (2021). Big data analytics capability for improved performance of higher education institutions in the Era of IR 4.0: A multi-analytical SEM & ANN perspective. Technological Forecasting and Social Change, 173, 121119.
Manogaran, G., Thota, C., & Lopez, D. (2022). Human-computer interaction with big data analytics. In Research Anthology on Big Data Analytics, Architectures, and Applications (pp. 1578-1596). IGI global.
Abkenar, S. B., Kashani, M. H., Mahdipour, E., & Jameii, S. M. (2021). Big data analytics meets social media: A systematic review of techniques, open issues, and future directions. Telematics and informatics, 57, 101517.
Teng, S., & Khong, K. W. (2021). Examining actual consumer usage of E-wallet: A case study of big data analytics. Computers in Human Behavior, 121, 106778.
Niu, Y., Ying, L., Yang, J., Bao, M., & Sivaparthipan, C. B. (2021). Organizational business intelligence and decision making using big data analytics. Information Processing & Management, 58(6), 102725.
Aljumah, A. I., Nuseir, M. T., & Alam, M. M. (2021). Traditional marketing analytics, big data analytics and big data system quality and the success of new product development. Business Process Management Journal, 27(4), 1108-1125.
Corsi, A., de Souza, F. F., Pagani, R. N., & Kovaleski, J. L. (2021). Big data analytics as a tool for fighting pandemics: a systematic review of literature. Journal of ambient intelligence and humanized computing, 12(10), 9163-9180.
Sudar, K. M., Nagaraj, P., Deepalakshmi, P., & Chinnasamy, P. (2021, January). Analysis of intruder detection in big data analytics. In 2021 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-5). IEEE.
Rahmani, A. M., Azhir, E., Ali, S., Mohammadi, M., Ahmed, O. H., Ghafour, M. Y., ... & Hosseinzadeh, M. (2021). Artificial intelligence approaches and mechanisms for big data analytics: a systematic study. PeerJ Computer Science, 7, e488.
Sheng, J., Amankwah‐Amoah, J., Khan, Z., & Wang, X. (2021). COVID‐19 pandemic in the new era of big data analytics: Methodological innovations and future research directions. British Journal of Management, 32(4), 1164-1183.
Nti, I. K., Quarcoo, J. A., Aning, J., & Fosu, G. K. (2022). A mini-review of machine learning in big data analytics: Applications, challenges, and prospects. Big Data Mining and Analytics, 5(2), 81-97.
Shakya, S., & Smys, S. (2021). Big data analytics for improved risk management and customer segregation in banking applications. Journal of ISMAC, 3(3), 235-249.
Zhang, J. Z., Srivastava, P. R., Sharma, D., & Eachempati, P. (2021). Big data analytics and machine learning: A retrospective overview and bibliometric analysis. Expert Systems with Applications, 184, 115561.
Awan, U., Shamim, S., Khan, Z., Zia, N. U., Shariq, S. M., & Khan, M. N. (2021). Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance. Technological Forecasting and Social Change, 168, 120766.
Monino, J. L. (2021). Data value, big data analytics, and decision-making. Journal of the Knowledge Economy, 12, 256-267.
Zhang, J., Qu, Z., Chen, C., Wang, H., Zhan, Y., Ye, B., & Guo, S. (2021). Edge learning: The enabling technology for distributed big data analytics in the edge. ACM Computing Surveys (CSUR), 54(7), 1-36.
Mangla, S. K., Raut, R., Narwane, V. S., Zhang, Z., & Priyadarshinee, P. (2021). Mediating effect of big data analytics on project performance of small and medium enterprises. Journal of Enterprise Information Management, 34(1), 168-198.
Kusal, S., Patil, S., Kotecha, K., Aluvalu, R., & Varadarajan, V. (2021). AI based emotion detection for textual big data: techniques and contribution. Big Data and Cognitive Computing, 5(3), 43.
Chen, Q., & Fang, H. (2022, August). Language Vitality Assessment Based on Cyberspace Data: A Case Study and Future Research Prospects. In International Conference on Computer Science and Education (pp. 167-179). Singapore: Springer Nature Singapore.
Huynh-Cam, T. T., Chen, L. S., & Huynh, K. V. (2022). Learning Performance of International Students and Students with Disabilities: Early Prediction and Feature Selection through Educational Data Mining. Big Data and Cognitive Computing, 6(3), 94.
Chen, M., & Yuan, Z. (2022). Teaching mode of english language and literature based on artificial intelligence technology in the context of big data. Mobile Information Systems, 2022, 1-11.
Cheng, J., Zhang, G., & Li, J. (2023, June). Beijing Language and Culture University, College Road No. 15 in Haidian District of Beijing, Beijing, China 1jm@ blcu. edu. cn. In Computer Science and Education: 17th International Conference, ICCSE 2022, Ningbo, China, August 18–21, 2022, Revised Selected Papers, Part III (p. 111). Springer Nature.
Alslaity, A., & Orji, R. (2022). Machine learning techniques for emotion detection and sentiment analysis: current state, challenges, and future directions. Behaviour & Information Technology, 1-26.
Zhao, W., Li, X., & Zhou, L. Research on performance evaluation and optimization of college budget management under the background of big data. Applied Mathematics and Nonlinear Sciences.
Chua, C. L. (2022). Developing a fengshui-based strategic decision-making model for Malaysia's property industry (Doctoral dissertation, University of Wales Trinity Saint David).
Leng, C. C. (2021). Developing a Fengshui-Based Strategic Decision-Making Model for Malaysia's Property Industry (Doctoral dissertation, University of Wales Trinity Saint David (United Kingdom)).
Alshammari, R. F. N., Arshad, H., Abd Rahman, A. H., & Albahri, O. S. (2022). Robotics utilisation in automatic vision-based assessment systems from artificial intelligence perspective: A systematic review. IEEE Access.
Aryadoust, V., Soo, Y. X. N., & Zhai, J. (2023). Exploring the state of research on motivation in second language learning: a review and a reliability generalization meta-analysis. International Review of Applied Linguistics in Language Teaching, (0).
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