7 articles for “Graph Neural Networks”

Set a search alert

Research ArticleOpen accessPhrase in the titleTAGN-IDC: Implicit Discrete Constraints for Tsunami Aware Graph Neural Networks in Spatial Evacuation RoutingSularno, Wendi Boy, Putri Anggraini et al.Tsunami evacuation route requires rapid identification of safe evacuation paths under dynamically changing hazard conditions. A major challenge is incorporating hard binary road-blockage constraints caused by tsunami inundation while maintaining end-to-end differentiability for deep learning models. Existing approaches commonly rely on continuous relaxations or stochastic sampling techniques, which either compromise physical feasibility or increase computational complexity. This study proposes the … …Journal of Computer Science, Vol. 22, Issue 9 · 24 Sep 2026 · 704 accessesView PDF Cite

Research ArticleOpen accessPhrase in the titleSystematic Literature Review of Graph Neural Networks in Disaster Management: Methods, Applications, and Future DirectionsSularno, Wendi Boy, Putri Anggraini et al.The increasing frequency and complexity of natural disasters underscore the urgent need for intelligent systems that support timely and effective decision-making. Graph Neural Networks (GNNs) have emerged as a powerful deep-learning paradigm for modeling spatial and relational data, offering distinct advantages for disaster management. This study presents a Systematic Literature Review (SLR) of GNN-based approaches for disaster mitigation, emergency response, … …Journal of Computer Science, Vol. 22, Issue 2 · 17 Feb 2026 · 2,036 accessesView PDF Cite

Research ArticleOpen accessEvery word in the titleIdentification of Influential Nodes With Topological Structure Via GRAPH Neural Network (GNN) Approach in Social Media NetworksRajnish Kumar, Laxmi Ahuja, Suman Mann, Sanmukh KaurThe amount of data in social networks is vast these days, and constantly changing, making influential node identification crucial. The existing topologies are constantly changing due to the evolving behavior of the applied dataset. Node and leaf topology or feature-based value form the basis for machine learning and centrality computation. Hence, influential node value determination is based on the node … …Journal of Computer Science, Vol. 22, Issue 2 · 17 Feb 2026 · 1,830 accessesView PDF Cite

Research ArticleOpen accessIn the keywordsDynamic Knowledge Driven Multi-Scale Spatiotemporal Fusion Framework for Reliable Multi-Horizon Traffic Flow ForecastingDeepika M S, P. Deepa Shenoy, Venugopal K RAccurate traffic flow forecasting remains a core challenge in intelligent transportation systems. Urban traffic patterns are characterised by complex spatiotemporal dependencies and inherent uncertainties, which complicate traffic forecasting. This study proposes a Dynamic Knowledge-Driven Spatiotemporal Fusion (DKDMSF) architecture to extract features of Multi-Scale traffic dynamics across various temporal traffic horizons. DKDMSF integrates Dynamic Spatial Attention (DSA) and Adaptive Graph Learning … …Journal of Computer Science, Vol. 22, Issue 9 · 30 Sep 2026 · 413 accessesView PDF Cite

Research ArticleOpen accessIn the abstractAsymptotic Behavior of an Artificial Neural Network Defined on Multipartite Directed GraphN. C. Majee, A. B. RoyProblem statement: Artificial Neural Network (ANN) are simple models to mimic some essential features of the complex central nervous system. ANN models are realistic due to their inherent stochastic nature of neural computation and strong synchronicity. Different ANN models are associated with directed and signed graphs. The present study proceeded by relaxing certain simplifying assumptions in the ANN model. Approach: … …OnLine Journal of Biological Sciences, Vol. 10, Issue 1 · 30 Jun 2010 · 7,476 accessesView PDF Cite

Research ArticleOpen accessAll words, across the recordEarly Plant Disease Detection Using Graph Isomorphic Networks: Enhancing Crop Yield Through Leaf AnalysisD. Sumathi, Sreejyothsna Ankam, Pravin Prakash Adivarekar et al.The economy of Tanzania is mostly driven by agriculture. Disease is one of the reasons that contributes to the low production of staple foods like cassava and maize, alongside climate change. Loss of income and food security are the results. In order to detect the diseases early, preventative measures are required. A potential option for farmers could be the use … …Journal of Computer Science, Vol. 21, Issue 9 · 15 Oct 2025 · 3,101 accessesView PDF Cite

Research ArticleOpen accessAll words, across the recordA Vertical Stacking-Based Ensemble Deep Learning Model for Early Diagnosis of Alzheimer's Disease Using Multimodal MRI ScansParvatham Niranjan Kumar, Lakshmana Phaneendra MaguluriEarly detection of Alzheimer's Disease (AD) is crucial for timely interventions that can slow disease progression, enhance quality of life, and assist with future planning. Convolutional Neural Networks (CNNs) are an efficient method for processing image-based data. In this work, we used CNN-based deep learning models to extract structural information from structural MRI (sMRI) and brain neuron connectivity patterns from … …Journal of Computer Science, Vol. 21, Issue 6 · 14 Jun 2025 · 3,099 accessesView PDF Cite