Abstract Cyber insurance transfers the residual cyber risk that remains after technical controls are installed, yet conventional pricing models rarely condition losses on grid topology, operating state, communication dependencies, and physical consequences. We develop a graph neural network (GNN) framework for pricing...
Mohannad Alhazmi, Olivia P. Li· Scientific Reports· 0 citations
The proposed chapter suggest a highly complex machine learning crime prediction model on an intelligent heterogeneous policing network, which will have to consider dynamic environment, multimodal, and real-time decision-making. A deep learning (CNN LSTM) hybrid architecture that is founded on graph neural networks and...
Pradeep Sambamurthy, Sanjeev Gour, S. Vamsee Krishna et al.· Advances in wireless technol...· 0 citations
As the world of smart, connected public safety networks expands in scale and sophistication, it is increasingly susceptible to attacks combining cyber and physical capabilities. The chapter suggests a novel framework for threat detection based on AI, which involves the fusion of multimodal intelligence, graph neural re...
Hastimal Jangid, Vandana Pushe, M. Vijayasanthi et al.· Advances in wireless technol...· 0 citations
Heterogeneous graph neural networks (HGNNs) have demonstrated exceptional capabilities in modeling complex relationships for recommendation tasks. Their integration with contrastive learning (CL) has recently garnered significant attention due to its ability to effectively capture both structural and semantic features,...
Lei Sang, Jia-Hao Cheng, Lin Mu et al.· IEEE Transactions on Systems...· 0 citations
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Distribution system state estimation provides the network-wide voltage awareness required to operate active feeders, yet scarce real-time metering forces reliance on uncertain pseudo-measurements. This paper studies how to attach empirically auditable, finite-sample prediction intervals to learned network-wide voltage...
Si-Qiang Zhao, Feng-Xiang Zhang· Energy and AI· 0 citations
In response to the bottleneck issues of energy consumption and carbon emissions caused by the explosive growth of the low-altitude economy, we break away from the traditional single-machine energy-saving research paradigm and innovatively reconfigure the sixth-generation mobile communication (6G) perception-integration...
Graph-based network science has evolved from its traditional foundations in discrete mathematics into an interdisciplinary field that integrates graph algorithms, network analysis, machine learning, artificial intelligence, knowledge representation, and temporal modelling. This study critically reviews this evolution,...
Rajasekhar Uppari, Mammen V Ashok· ACR North American Advances· 0 citations
Install Grab a binary from the table: Windows Linux Mac classic (High Sierra or above) Mac M1 Matlab R2018b or later R2018b or later R2018b or later R2023b or later (Apple Silicon) R2018b or later (Rosetta) Octave 6.2.0 or later 6.2.0 or later 6.2.0 or later 6.2.0 or later Python pip install casadi (needs pip -V>=8.1)...
Joel A E Andersson, Joris Gillis, Greg Horn et al.· Zenodo (CERN European Organi...· 0 citations
The findings indicate that neural architectures particularly recurrent networks, autoencoders, transformers, and graph neural networks provide superior adaptability to evolving fraud behaviour compared with static rule-based systems, but their operational deployment is constrained by class imbalance, concept drift, and...
Juliet Nwanneka Amoke· International Journal of Com...· 0 citations
This study examines a Graph Neural Network (GNN)-based approach for controlling communication topology and coordinated motion in self-organizing wheeled robot swarms under dynamically changing spatial and network conditions. The proposed framework represents robots as graph nodes and wireless communication links as gra...
Description: Complete reproducibility records for “Graph neural networks and transformers for antimalarial drug discovery: honest-negative results underdistribution shift” submitted to the Journal of Computer-Aided Molecular Design (JCAMD). This deposit provides: - Core dataset (19,836 molecules from African natural pr...
Myke Vital Sao Temgoua, J.-P. Tchapet Njafa, Samafou Penabeï et al.· 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
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Microsoft Research Blog· microsoft.comJul 13, 2026
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduJul 6, 2026
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
