The increasing adoption of cloud computing has driven organizations to deploy applications and services across multiple cloud (multi-cloud) platforms, leading to multi-cloud environments rise. While this approach enhances flexibility, scalability, and resilience by mitigating vendor lock-in, it also introduces significant security challenges as data confidentiality, integrity, access control, and secure interoperability between heterogeneous platforms due to the heterogeneity of clouds providers. Ensuring consistent and robust security across diverse infrastructures requires a unified and adaptive data storage security architecture. In this respect, we propose in this work a multi objective optimization Zero-Trust-based hybrid intelligent edge-fog-multi-cloud Data Storage security architecture. For this purpose, we consider the fundamental properties of cloud security: availability, confidentiality, integrity, authenticity, and privacy. These properties are integrated into multi-objective problems, enabling strong criteria. It is a novel architecture formulated as multi-objective problems tackling resources management within every single cloud of the multi-cloud system using a load balancing technique, energy optimization approach within the cloud data centres, and homomorphic security approach of the multi-cloud to avoid sensitive data exposure.
Raja Ait El Mouden, Ahmed Asimi, Y. Asimi· EPJ Web of Conferences· 0 citations
Globally, pulmonary diseases are a major health burden, especially in settings where resources are constrained and access to specialized radiological expertise is limited. Most chest radiograph-based deep learning models rely only on imaging data, despite showing promising performance when it comes to their diagnostic capabilities. However, clinical decision-making in the real world integrates structured patient information with the aforementioned imaging data. Our study aims to compare current multimodal machine learning approaches that combine clinical data and imaging used in pulmonary disease classification and propose a deployable framework designed for clinical settings in resource-constrained environments. We conducted a review of recent literature around multimodal AI in pulmonary diseases, and we focused on fusion strategies (early-stage, late-stage, and hybrid), techniques for data integration, validation settings, as well as deployment considerations. We performed a comparative synthesis aiming to identify methodological patterns, translational gaps, and performance trends, and on this basis, as a conceptual blueprint to guide our subsequent model development, we propose a modular architecture integrating structured-data encoders with convolutional neural networks for imaging data, along with fusion mechanisms to handle incomplete modalities. We then formalize the fusion operators and present an algorithm for graceful degradation under missing clinical data; empirical validation of the proposed architecture is deferred to a future work. This review indicates that multimodal approaches consistently outperform unimodal imaging models, especially in early-stage or complex cases, with gains in performance reported across many pulmonary conditions. However, the review also reveals several limitations, whether it's the lack of standardized fusion evaluation, insufficiencies in external validation, inadequacies in the handling of missing clinical variables, or limited attention to real-world clinical integration. These obstacles expose the need for system design that is deployment-aware more so than solely performance-driven optimization. Through this work, we aim to contribute a structured synthesis of multimodal pulmonary AI and outline an interpretable, resource-conscious framework intended for integration into healthcare workflows, which we put forward as the design basis for a model to be developed and evaluated in future work. By aligning the model design philosophy with the realities of clinical workflows, this approach should support equitable access to AI-assisted diagnostics and help advance the application of AI for healthcare improvement and social good.
Hamza Hrid, M. Machkour, Y. Asimi· EPJ Web of Conferences· 0 citations
SplittingFed-DP relocates the Gaussian DP mechanism from the high-dimensional gradient to the low-dimensional activation space at the cut layer, audited under Rényi differential privacy and proves that this same Gaussian release coincides with the randomised-smoothing operator of Cohen et al. at the cut layer.
Rguibi Arjdal, Y. Asimi, Ahmed Asimi et al.· EPJ Web of Conferences· 0 citations
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