2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 10 , Issue 2 , PP: 95-107, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Rana K. A. Ahmed 1 * , Ryham Ali Zubaid 2 , Fay Fadhil 3 , Israa Habeeb Naser 4
Doi: https://doi.org/10.54216/FPA.100209
the impact of social analytics on hospital health management: a multilevel fusion approach for data-driven decision-making and brand improvement. The hospital health management center should use feature extraction techniques to learn more about customers' feelings towards their services and optimize their business strategies and promotions accordingly. The proposed multi-level/hybrid level fusion system architectures can effectively integrate data/images from multiple sources, including social networks, to collect and process essential data for score level and rank level decision-making. This approach leverages intelligent techniques, such as deep learning models, fuzzy logic, and optimization algorithms, to improve fusion scores and achieve optimal fusion performance. The proposed framework can also be extended to various applications, including multimedia data fusion, e-systems data fusion, and spatial data fusion, to enable intelligent systems for information fusion and decision-making in diverse domains. Therefore, this paper proposes Improved Customer Relation and Business Operations (ICR-BO) to enhance customer relationships in business development using text and social analytics. A case study is carried out to explore the online debate of computer brands operated in hospital environments and Twitter suppliers. The authors used text-mining strategies and social analytics to analyze business operations. Social Media uses data sets to view important observations and trends to identify consumer awareness after collecting critical tweets using Twitter search. The experimental results show that ICR-BO achieves the highest customer relation compared to other existing methods.
Business Operations , Customer , Health Management , Fusion techniques , Social Analytics , Text Multilevel Fusion.
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