The growing use of interconnected and digital systems in clinical settings has increased the necessity of smart and robust protection systems that can assume extremely rigid privacy and reliability requirements. This paper presents the GuardianMesh: Anomaly-Resilient Federated Orchestration (GM-ARFO) a new AI-based threat prevention model that can be used to provide security to the world of distributed healthcare information ecosystems. The method proposed will allows collaborative intelligence between heterogeneous medical nodes and does not present sensitive patient information or centralised control. GuardianMesh (GM) works by using local clinical and system cues to create compact privacy preserving representations in the edge and then a detection of anomalous behaviors is possible early on. These depictions are jointly trained in an effective federated orchestration system that is resilient to adversarial manipulation and communication inefficiently. A dec-layer adjudication layer is what is used to package distributed evidence of anomalies to facilitate swift and automatic response procedures with have minimum impact to clinical processes. Moreover, adaptive monitoring adapts to behavior drift and the changing attack plans all the time, ensuring the reliability of detection over a long period. Thorough tests in various conditions of operation and adversary show that GM-ARFO has a high level of detection, low false alarms, lower response time in addition to maintaining data confidentiality. The findings support the fact that the suggested GuardianMesh framework offers a scalable, future-restaurant, and privacy-aware platform of ensuring the safety of next-generation healthcare information infrastructures. The suggested method attains an overall detection accuracy of 96.8%, indicating a highly dependable identification of anomalous behaviours in remote healthcare systems.
Ramgopal Kashyap, Vrince Vimal, Vikalp Sharma et al.· 2026 International Conferenc...· 0 citations
Sound prediction of existence of future generation based on stochastic sources is a critical issue because of high nonlinearities, high transitions in the environment, and the nature of inherent uncertainty in observational information. The paper proposes an integrated architecture of deep learning, which is the Hierarchical Regime-Adaptive Probabilistic Network (HRAPN) that aims to overcome these constraints using an end-to-end learning framework. The solution selection boasts of hierarchical representation induction with a latent regime adaptation mechanism that modulates dynamically the internal model behavior in a non-stationary environment. Besides that, attention guided dependency synthesis module performs informative temporal context aggregation selectively to allow an efficient long horizon modeling without impaired performance. In contrast to the more traditional deterministic approaches, HRAPN uses probabilistic model of output in order to explicitly model predictive uncertainty, which enhances robustness and reliability of the estimated decisions. The system takes directly heterogeneous and multivariate data, without explicit features or domain pre-treatment. It is experimentally assessed that the presented method achieves higher results as compared to existing baselines in accuracy, stability, and uncertainty calibration in various forecast periods. The findings validate the performance of regime perceiving, hierarchical abstraction and probabilistic inference in the same learning process. The proposed HRAPN model offers a scaffoldable and adaptable evaluation of the dynamic generation modeling under both variable operating conditions with data. The suggested framework attains an overall accuracy of 96.6%, illustrating its robust prediction reliability and exceptional performance relative to current methodologies.
Bal Krishna Saraswat, Sonu Lal, Anshu Malhotra et al.· 2026 International Conferenc...· 0 citations