Next-Gen AI-Cloud Ecosystems: Leveraging Machine Learning, Computational Knot Theory, and Image Denoising for Modernization
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Abstract
The rapid evolution of enterprise IT ecosystems demands advanced frameworks that integrate artificial intelligence (AI), cloud-native architectures, and cutting-edge computational techniques. This paper presents a Next-Generation AI-Cloud Ecosystem designed for scalable modernization across diverse industry domains. By leveraging machine learning (ML) and deep learning (DL) models, the platform enables predictive analytics, anomaly detection, and intelligent process automation. In addition, computational knot theory is employed for complex topological data analysis, offering novel approaches to network optimization and secure data structuring. Image denoising techniques further enhance the quality of visual and medical datasets, ensuring accurate downstream analytics. The proposed ecosystem demonstrates significant improvements in operational efficiency, data integrity, and adaptive intelligence, providing a holistic blueprint for enterprise modernization in the AI-driven era.
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