graph neural networks

Reliability-aware ensemble recommendation for open-network commerce: a dual-metric framework for jointly evaluating ranking accuracy and serving correctness

Recommendation quality in open-network commerce is conventionally measured through offline ranking metrics that implicitly assume reliable upstream retrieval, stable item identities, and consistent catalog availability. In open networks such as India's Open Network for Digital Commerce (ONDC), these assumptions are rou...

Distilling Trial-Level EEG Variability into Participant Representations: Neurophysiologically Constrained Graph Learning in ADHD

Extracting stable individual-level representations from noisy and variable trial-wise EEG remains a major challenge in computational cognitive neuroscience. We developed a spatiotemporal graph attention network to analyze Go/NoGo EEG from children and adolescents with and without ADHD. The framework combines ERP-inform...

Plug and schedule: scalable and modular architecture for integrated and dynamic scheduling based on graph neural networks and reinforcement learning

A Comparative Analysis of Machine Learning Techniques for Power, Performance, and Area Optimization in Low-Power VLSI Design

The continuous scaling of semiconductor technology has significantly increased the complexity of Very Large-Scale Integration (VLSI) design. Modern integrated circuits are required to deliver high computational performance while operating within strict power and area constraints. Traditional optimization approaches bas...

Anomaly detection in SAP S/4HANA using hybrid deep learning for multivariate time series data

Inhomogeneous data structure, very sparse abnormal events, non-stationary temporal behaviour, and multifaceted cross-module relationships are some of the main challenges facing anomaly detection of an enterprise application like SAP S/4HANA. The current multivariate time-series anomaly detection methods, mostly designe...

Dual stream spatiotemporal feature learning framework for neurological disease severity level classification using fMRI data

Neurological disorders such as Alzheimer’s disease, epilepsy, and Parkinson’s disease are often characterized by subtle yet progressive alterations in brain activity that can be effectively studied through functional magnetic resonance imaging (fMRI). Accurate classification of disease severity is critical for clinical...

Graph neural network surrogates for simulation of urban drainage systems containing orifice actuators

Study region The urban drainage system (UDS) in central Yueyang city, China. Study focus Surrogate models for UDSs can reduce computational costs. Graph neural networks (GNNs), which capture the topological structure and intrinsic relationships within UDSs, are natural candidates for constructing surrogate models. Howe...

Unified Multitask Model for Aquatic Toxicity Prediction across Organism Groups and Effect End Points: A Comparison of Molecular Representations

Abstract Computational prediction of aquatic toxicity provides a rapid and cost-efficient alternative to resource-intensive in vivo ecotoxicological testing. However, most existing models adopt a fragmented modeling strategy, developing separate models for each organism group and toxicity end point. Such task-specific...

TriPhy-GF—A Triple-Physics-Constrained Graph Neural Network for Accurate and Efficient Microseismic Event Localization in Coal Mines

Machine Learning and Deep Learning Approaches for Flood Prediction

Flooding remains one of the most damaging natural hazards worldwide, and the growing availability of hydrological sensor networks and satellite data has pushed researchers toward data-driven alternatives to conventional physics-based hydrological models. This paper synthesizes findings from six recent bodies of work —...

AI-Driven Threat Detection Architecture

The rapid proliferation of digital infrastructure, spanning cloud computing, the Internet of Things (IoT), edge networks, and mobile platforms, has dramatically expanded the attack surface available to cybercriminals. Traditional signature-based intrusion detection systems, while effective against known threats, are fu...

Multi-UUV radiated-noise spectral prediction in shallow water using neural field priors and graph residual learning

Predicting the received radiated-noise power spectral density (PSD) of multiple unmanned underwater vehicles (UUVs) in shallow-water waveguides is challenging. The measured spectrum is jointly affected by incoherent multi-source power superposition, propeller rotation speed, source–receiver geometry, and multipath prop...