This whitepaper proposes a privacy-preserving federated graph intelligence architecture for fraud detection across Kenya’s account-to-account payment ecosystem, using PesaLink as the principal infrastructure context. The research addresses a structural limitation of institution-level fraud detection: fraudulent activit...
Nevil Maloba· Zenodo (CERN European Organi...· 0 citations
This whitepaper proposes a privacy-preserving federated graph intelligence architecture for fraud detection across Kenya’s account-to-account payment ecosystem, using PesaLink as the principal infrastructure context. The research addresses a structural limitation of institution-level fraud detection: fraudulent activit...
Nevil Maloba· Zenodo (CERN European Organi...· 0 citations
This repository contains the source code and reproducibility materials associated with the Privacy-Preserving Federated Ensemble Optimization (PP-FEO) framework for intrusion detection in distributed Cyber-Physical System (CPS) environments. The implementation includes data preprocessing and feature harmonization, the...
Dhanalakshmi B, P Tamije Selvy· Zenodo (CERN European Organi...· 0 citations
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This repository contains the source code and reproducibility materials associated with the Privacy-Preserving Federated Ensemble Optimization (PP-FEO) framework for intrusion detection in distributed Cyber-Physical System (CPS) environments. The implementation includes data preprocessing and feature harmonization, the...
Dhanalakshmi B, P Tamije Selvy· Zenodo (CERN European Organi...· 0 citations
A novel federated graph reinforcement learning-based digital twin predictive intelligence, blockchain trust management, and adaptive self-healing control integrated framework called Energy Efficient Federated Digital Twin Framework (EEFDTF) for Blockchain-Enabled Self-Healing Smart Grids to overcome challenges.
Anuj Mangal, Gunaselvi Manohar, D. Seenivasan et al.· International Journal of Com...· 0 citations
This review examines the transformative impact of artificial intelligence (AI) and machine learning on composite materials design, property prediction, and optimisation and provides a structured classification of AI/ML techniques in composite materials.
Sudhanshu Singh, Ranjna Kumari, Zunjarrao Kamble et al.· Journal of reinforced plasti...· 0 citations
With the rapid development of the data element market, big data transaction circulation has put forward higher requirements for cross-domain collaborative modeling and privacy protection. Existing methods can reduce the exposure of raw data, but still have shortcomings in non-independent and identically distributed dat...
Yalin Hu, Xu Bin, Pan Zhou et al.· Advances in transdisciplinar...· 0 citations
This dataset package is associated with the study “HAPV-FEL: Historical Consensus Verification and Recoverable Trust for Privacy-Preserving Federated Transaction Risk Detection.” It contains structured CSV representations corresponding to the IBM AML and SAML-D datasets used for cross-institutional transaction risk det...
This chapter surveys how digital twins (DTs) and intelligent manufacturing are reshaping the lifecycle of breast-cancer medical devices through Internet of Things (IoT)-driven data flows. We review architectures that link design, production, clinical use, and post-market surveillance via a “digital thread,” enabling co...
Farah Hesham, Mohammed Mostafa, Mohammed Abdalla et al.· Handbook of Smart Healthcare· 0 citations
Reproducibility package for C-ProVFL, a selective safe-output controller for backdoor attacks in vertical federated learning. The archive contains source notebooks, the immutable submitted-configuration manifest, corrected split and attack manifests, frozen-policy records, row-level results for 768 configurations, matc...
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.