INNOVATING WITH SPRING BOOT: AI-POWERED APPLICATIONS FOR MODERN BUSINESSES
Keywords:
Enterprise Application Development, Artificial Intelligence Integration, Cloud-Native Architecture, Machine Learning Automation, Distributed Computing SystemsAbstract
Spring Boot has emerged as a transformative framework in the enterprise technology landscape, particularly in its integration with artificial intelligence capabilities. This paper examines how organizations across various industries are leveraging Spring Boot to create sophisticated AI-powered applications that enhance operational efficiency and competitive advantage. The research explores the framework's impact on development processes, scalability, and system performance across financial services, e-commerce, and customer service sectors. Through analysis of real-world implementations, the study demonstrates Spring Boot's effectiveness in simplifying AI integration while maintaining robust security and compliance standards. The paper also investigates emerging trends in AutoML integration, edge computing adoption, and federated learning approaches, providing insights into future development paradigms and opportunities in the Spring Boot ecosystem.
References
Aizhan Tursunbayeva and Hila Chalutz-Ben Gal, “Adoption of artificial intelligence: A TOP framework-based checklist for digital leaders,” Business Horizons, Volume 67, Issue 4, Pages 357-368, July–August 2024. Available: https://www.sciencedirect.com/science/article/pii/S000768132400051X
Samuli Laato, et al, “Trends and Trajectories in the Software Industry: implications for the future of work.” Information Systems Frontiers, 2023. Available: https://link.springer.com/article/10.1007/s10796-022-10267-4
Namkyu Lim, Tae-gong Lee and Sang-gun Park, “A Comparative Analysis of Enterprise Architecture Frameworks Based on EA Quality Attributes,” January 2009. Available: https://www.researchgate.net/publication/220908600_A_Comparative_Analysis_of_Enterprise_Architecture_Frameworks_Based_on_EA_Quality_Attributes
Mythily Ganesh, et al, “An Analysis of the Significance of Spring Boot in The Market,” July 2022. DOI:10.1109/ICICT54344.2022.9850910. Available: https://www.researchgate.net/publication/362747012_An_Analysis_of_the_Significance_of_Spring_Boot_in_The_Market
Annadurai Vinothkanna, et al, “Advanced detection tools in food fraud: A systematic review for holistic and rational detection method based on research and patents,” Food Chemistry, Volume 446, 15 July 2024, 138893. Available: https://www.sciencedirect.com/science/article/pii/S0308814624005429
Suyash Joshi, “Monitor Spring Boot Web Application Performance Using Micrometer and InfluxDB,” DZone, Dec. 24, 2024. Available: https://dzone.com/articles/monitor-spring-boot-web-application-performance
Philip Jorzik, et al, “AI-driven business model innovation: A systematic review and research agenda,” Journal of Business Research, Volume 182, September 2024. Available: https://www.sciencedirect.com/science/article/pii/S0148296324002686
Olumide Adewole, “SCALABILITY IN ARTIFICIAL INTELLIGENCE,” November 2023. Available: https://www.researchgate.net/publication/375370072_SCALABILITY_IN_ARTIFICIAL_INTELLIGENCE
Imrus Salehin, et al, “AutoML: A systematic review on automated machine learning with neural architecture search,” Journal of Information and Intelligence, Volume 2, Issue 1, Pages 52-81, January 2024. Available: https://www.sciencedirect.com/science/article/pii/S2949715923000604
Qi Xia, et al, “A survey of federated learning for edge computing: Research problems and solutions,” High-Confidence Computing, Volume 1, Issue 1, June 2021. Available: https://www.sciencedirect.com/science/article/pii/S266729522100009X