Current Issue : October-December Volume : 2025 Issue Number : 4 Articles : 5 Articles
Financial institutions are rapidly transforming their operations through cloud adoption while facing intensified cybersecurity challenges and regulatory requirements. The migration to cloud infrastructure demands sophisticated architectural patterns and robust security controls to maintain compliance and operational efficiency. Multi-cloud architectures incorporating shared services models, centralized identity management, and automated compliance mechanisms have emerged as essential frameworks for secure cloud operations. Organizations implementing comprehensive security strategies demonstrate improved threat detection, reduced incident response times, and enhanced compliance capabilities while maintaining business agility....
The growing demand for efficient human action recognition has led to significant advancements in applications such as surveillance, healthcare, and human–computer interaction. This paper uses theWidar3.0 dataset to evaluate the proposed a cloud–edge collaborative learning framework that incorporates model compression techniques, including knowledge distillation, pruning, and quantization, to optimize computational resource usage and improve recognition accuracy. Focusing on human action recognition, the framework was structured across two layers: the cloud and the edge devices, each handling specific tasks such as global model updates, intermediate model aggregation, and lightweight inference, respectively. Experimental results showed that the proposed framework achieved an accuracy of 89.7%, outperforming traditional models by 4.4%. Moreover, the communication overhead per round was reduced by 43%, decreasing from 100 MB to 57 MB. The framework demonstrated improved performance as the number of edge devices increased, with the accuracy rising to 89.0% with 10 devices. These results validate the effectiveness of the proposed system in achieving high recognition accuracy while significantly reducing resource consumption for human action recognition....
This article explores the strategic integration of edge computing with cloud infrastructure to optimize distributed deployments in modern application architectures. As organizations increasingly adopt low-latency applications across industries, edge computing has emerged as a complementary paradigm to traditional cloud computing, enabling data processing closer to the source. The article examines the architectural principles of the edge-cloud continuum, highlighting hierarchical models that balance centralized coordination with distributed processing. It investigates container orchestration paradigms using lightweight Kubernetes distributions optimized for edge environments, detailing multi-cluster management strategies and intelligent workload placement techniques. The article further explores synchronization techniques that maintain consistency between edge and cloud components despite connectivity challenges, and presents performance optimization frameworks including distributed tracing, content caching, and autonomous operation capabilities. Throughout the discussion, the synergistic relationship between edge and cloud is emphasized as critical for building resilient, responsive, and scalable applications in today's distributed computing landscape....
Enterprise organizations are increasingly adopting multi-cloud and hybrid cloud strategies to enhance operational efficiency, ensure business continuity, and avoid vendor lock-in. This comprehensive article examines the evolution of cloud computing strategies, focusing on implementation challenges, security considerations, and best practices in multi-cloud environments. The article investigates key aspects, including API portability, traffic management, load balancing, and security frameworks across cloud providers. Through extensive research and analysis of industry data, this article demonstrates how organizations can optimize their cloud infrastructure through standardized processes, automation, and strategic planning. The article reveals significant improvements in system reliability, cost optimization, and operational efficiency through the proper implementation of multi-cloud architectures and cloud-agnostic approaches....
Real-time artificial intelligence predictive analytics systems in cloud-based healthcare environments are comprehensively explored in this article. It examines the technical architecture, implementation challenges, and clinical outcomes of systems designed for early detection of critical conditions such as sepsis and acute cardiac events. The integration of streaming data processing, machine learning algorithms, and cloud infrastructure creates powerful tools that can significantly reduce mortality and morbidity through timely interventions. The article delves into architectural frameworks, data pipeline engineering, model selection considerations, inference optimization, clinical workflow integration, performance validation protocols, regulatory compliance requirements, and emerging trends in the field. Healthcare technology professionals will find essential insights for successful implementation strategies, addressing common obstacles, and understanding future development directions for predictive healthcare systems....
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