Using plithogenic n-SuperHyperGraphs to assess the degree of relationship between information skills and digital competencies by Salomón Marcos Berrocal Villegas, Willner Montalvo Fritas, Carmen Rosa Berrocal Villegas, María Yissel Flores Fuentes Rivera, Roberto Espejo Rivera, Laura Daysi Bautista Puma, Dante Manuel Macazana Fernández

“…Using Plithogenic n-SuperHyperGraphs as a modeling mechanism, 40 students were assessed through questionnaires and practical tests before and after a ten-week experiment. …”
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Robot-Based Procedure for 3D Reconstruction of Abdominal Organs Using the Iterative Closest Point and Pose Graph Algorithms by Birthe Göbel, Jonas Huurdeman, Alexander Reiterer, Knut Möller

“…Image-based 3D reconstruction enables robot-assisted interventions and image-guided navigation, which are emerging technologies in laparoscopy. …”
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Representation of chemistry transport models simulations using knowledge graphs by Eduardo Illueca Fernández, Antonio Jesús Jara Valera, Jesualdo Tomás Fernández Breis

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Early detection of cognitive decline with deep learning and graph-based modeling by Sunita Patil, Swetta Kukreja

“…In today’s world, increasing stress and depression significantly impact cognitive well-being, making early detection of cognitive impairment essential for timely intervention. This work introduces a Multimodal Fusion Cognitive Assessment Framework that leverages advanced deep learning and graph intelligence to enhance early identification accuracy. …”
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Graph-Aware Multimodal Deep Learning for Classification of Diabetic Retinopathy Images by Amina Zedadra, Ouarda Zedadra, Mahmoud Yassine Salah-Salah, Antonio Guerrieri

“…DRdiag integrates multiple modalities: the Fundus images and the duration of disease evolution which is an important factor in the diagnosis of DR as the duration of the disease directly influences the onset and progression of retinal lesions, leveraging two distinct models: a Convolutional Neural Network (CNN) based on DenseNet121 for image feature extraction and a Graph Neural Network (GNN) for capturing complex relationships between patient features. …”
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Infectious disease control as network interventions by Akihiro Nishi, George Dewey, Michael Mengual, Hiroyasu Ando, Nicholas Cassol-Pawson, Akira Endo

“…Abstract During the COVID-19 pandemic, non-pharmaceutical interventions (NPIs) were implemented globally to mitigate the spread of the infection. …”
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Evidence Graph Analysis of Postoperative Pain Sensitization Induced by Perioperative Sleep Deprivation by XUE Jianjun, WANG Caihong, GUO Lingling, LI Xiuxia, ZHANG Jie, XU Ziqing, HOU Huaijing, YANG Kehu

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Graph-Based Prediction of Spatio-Temporal Vaccine Hesitancy From Insurance Claims Data by Sifat Afroj Moon, Rituparna Datta, Tanvir Ferdousi, Hannah Baek, Abhijin Adiga, Achla Marathe, Anil Vullikanti

“…We find that an aggregated contact network or graph, developed from a detailed activity-based population network, plays an important role in the performance of VaxHesSTL, compared to graph models based solely on spatial proximity. …”
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BioKGrapher: Initial evaluation of automated knowledge graph construction from biomedical literature by Henning Schäfer, Ahmad Idrissi-Yaghir, Kamyar Arzideh, Hendrik Damm, Tabea M.G. Pakull, Cynthia S. Schmidt, Mikel Bahn, Georg Lodde, Elisabeth Livingstone, Dirk Schadendorf, Felix Nensa, Peter A. Horn, Christoph M. Friedrich

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Comparative analysis of the global innovation index combining the graph visualization and topological data analysis approaches by Rolando Ismael Yépez, Maricela Fernanda Ormaza

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Graph-based machine learning for high-resolution assessment of pedestrian-weighted exposure to air pollution by Feifeng Jiang, Jun Ma

“…Applied to New York City, the framework leverages graph-based machine learning to predict street-level PM2.5 concentrations from vehicle-sensed pollution data, while estimating high-resolution pedestrian volume derived from street view imagery and ground-truth count data. …”
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Edge-based modelling for disease transmission on random graphs: an application to mitigate a syphilis outbreak by Sicheng Zhao, Sahar Saeed, Megan Carter, Bradley Stoner, Maggie Hoover, Hugh Guan, Felicia Maria G. Magpantay

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ET-GNN: Ensemble Transformer-Based Graph Neural Networks for Holistic Automated Essay Scoring by Hind Aljuaid, Areej Alhothali, Ohoud Alzamzami, Hussein Assalahi, Tahani Aldosemani

“…Automated Essay Scoring (AES) offers a solution by automatically evaluating essays, reducing the need for human intervention. This paper presents a hybrid method, called Ensemble Transformer-Based Graph Neural Networks (ET-GNN), which integrates Transformer-based models with Graph Convolutional Networks (GCNs) for holistic AES. …”
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Exploring Multiscale Causal Interventions for Burned Area Estimation by A. Mustofa Irawan, A. Mustofa Irawan, M. Vall-llossera, M. Vall-llossera, C. López-Martínez, C. López-Martínez, A. Camps, A. Camps, A. Camps, G. Portal, M. Pablos, A. Alonso-González

“…Time-series causal graphs derived from satellite and reanalysis data identified 500-hPa geopotential height anomalies (ΔZ₅₀₀) as the primary driver of surface aridity and wildfire incidence. …”
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On the Optimal Control of Intervention Strategies for Hepatitis B Model by Abdulfatai Atte Momoh, Abubakar Audu, Déthié Dione, Inalegwu Michael Ali

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Integration of graph neural networks and long short-term memory models for advancing heart failure prediction by Ibrahim Alrashdi, Ahmed I. Taloba

“…This paper introduces a novel approach to predicting HF, integrating graph neural networks (GNNs) with long short-term memory (LSTM) networks for better prediction accuracy. …”
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Community Clustering and Recommendation Toward Elderly Health Records: Probabilistic Representation Learning and Large Graph Search by Huijiong Ding

“…This approach enables more precise, efficient, and personalized healthcare interventions, wherein the key steps include: 1) Geometry-based feature selection from high-dimensional health data-This effectively reduces dimensionality while preserving discriminative health patterns. 2) Probabilistic multi-topic representations of individual health profiles-These latent space embeddings capture complex health-need interactions. 3) Graph-based community detection via shared clinical/behavioral traits-The algorithm identifies natural clusters with medical relevance. 4) Personalized intervention ranking by community relevance-This ensures tailored care plan recommendations for each subgroup. …”
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Revolutionizing Education: Harnessing Graph Machine Learning for Enhanced Problem-Solving in Environmental Science and Pollution Technology by R. Krishna Kumari

“…Amidst the shifting tides of the educational landscape, this research article embarks on a transformative journey delving into the fusion of theoretical principles and pragmatic implementations within the realm of Graph Machine Learning (GML), particularly accentuated within the sphere of nature, environment, and pollution technology. …”
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Graph Neural Networks for Analyzing Trauma-Related Brain Structure in Children and Adolescents: A Pilot Study by Harim Jeong, Minjoo Kang, Shanon McLeay, R. J. R. Blair, Unsun Chung, Soonjo Hwang

“…This study explores the potential of graph neural networks (GNNs) in analyzing brain networks of children and adolescents exposed to trauma, addressing limitations in traditional neuroimaging approaches. …”
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EEG-based functional and effective connectivity patterns during emotional episodes using graph theoretical analysis by Majid Roshanaei, Hamzeh Norouzi, Julie Onton, Scott Makeig, Alireza Mohammadi

“…Graph theoretical analysis revealed significant differences in connectivity between emotions across multiple frequency bands, particularly in the delta and beta bands. …”
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