Finding Key Nodes in Complex Networks Through Quantum Deep Reinforcement Learning by Juechan Xiong, Xiao-Long Ren, Linyuan Lü

“…We compared the proposed algorithm with some classical node ranking and network dismantling algorithms on various synthetical and empirical networks. …”
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CGNLib: A Python library for Girvan–Newman community detection with customizable node-based centrality metrics by T. Punnapathiran, C. Angsuchotmetee, P. Kaewkarndee, K. Lavangnananda

“…CGNLib is a Python library designed to enhance the performance of community detection in networks using the Girvan–Newman algorithm. Traditional implementations of this algorithm typically rely solely on edge betweenness centrality, limiting the potential for optimization. …”
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An Analysis of Semi-Supervised Machine Learning in Electrical Machines by V. Raju Arvind, S. Shyamsharan, Poorvajaa Gurunathan, Krishna Kumba, Nawin Ra

“…SSML provides a key benefit in enhancing the effectiveness and precision of predictive models for optimizing electrical machine performance, reliability, and maintenance by leveraging labeled and unlabeled data. The research investigates important SSML algorithms such as self-training, co-training, generative models, and graph-based methods, highlighting their particular uses in fault diagnosis, condition monitoring, and predictive maintenance of electrical machines. …”
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Bridge Digital Twin for Practical Bridge Operation and Maintenance by Integrating GIS and BIM by Yan Gao, Guanyu Xiong, Ziyu Hu, Chengzhang Chai, Haijiang Li

“…However, traditional bridge management systems (BMS) and existing DT applications typically rely on standalone building information modeling (BIM) or geographic information system (GIS) platforms, with limited integration between BIM and GIS or consideration for their underlying graph structures. This study addresses these limitations by developing an integrated DT system that combines WebGIS, WebBIM, and graph algorithms within a three-layer architecture. …”
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Attention-Based Hypergraph Neural Network: A Personalized Recommendation by Peihua Xu, Maoyuan Zhang

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Real-Time Adaptive Traffic Flow Prediction Based on a GE-GRU-KNN Model by Xiangyu YI, Hongmei ZHOU, Shaopeng ZHONG

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ZenLDA: Large-Scale Topic Model Training on Distributed Data-Parallel Platform by Bo Zhao, Hucheng Zhou, Guoqiang Li, Yihua Huang

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Methodology for detecting anomalies in cyber attack assessment data using Random Forest and Gradient Boosting in machine learning by A. S. Kechedzhiev, O. L. Tsvetkova, A. I. Dubrovina

“…Developing effective methods for detecting anomalies and protecting against cyber threats is becoming a priority for organizations. Method. The research is carried out using two machine learning algorithms: Random Forest and gradient boosting. …”
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Feature Selection Algorithms Combined with Experimental Validation Reveal COL10A1 and TMPRSS4 Genes as Potential Diagnostic Biomarkers in Pancreatic Ductal Adenocarcinoma by Neda Shajari, Amin Ramezani, Ahmad Tahmasebi, Mohammad Hossein Anbardar, Zahra Roshanizadeh, Abbas Ghaderi

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New Strategies Based on Hierarchical Matrices for Matrix Polynomial Evaluation in Exascale Computing Era by Luisa Carracciuolo, Valeria Mele

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A New Dual Steering System in a Compact Tractor by Giorgio Paolo Massarotti, German Filippini, Gustavo Raush Alviach, Pedro Javier Gamez-Montero, Esteban Codina Macia

“…The goal of this work is to provide guidance on design methods and, through the Bond Graph-3D model, suggest the best control algorithms to minimize steering noise and enhance driving comfort. …”
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Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data by Marco Stock, Corinna Losert, Matteo Zambon, Niclas Popp, Gabriele Lubatti, Eva Hörmanseder, Matthias Heinig, Antonio Scialdone

“…We categorize common types of prior knowledge, such as experimental data and curated databases, and discuss methods for representing priors, particularly through graph structures. In addition, we classify recent GRN inference algorithms based on their ability to incorporate these priors and assess their performance in different contexts. …”
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Comparative analysis of the Traditional, Reverse and European Centre for Disease Prevention and Control algorithms and utilization of WHO NET software for syphilis serodiagnosis in... by Abirami Lakshmy Jayachandran, Sankara Dhanapaul, Visuvasam Joshua, Balan Kandasamy

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Design of a TSR-based project learning strategy for biochemistry undergraduate teaching and research labs: a case study by Camille R. Reaux, Shelby A. Meche, Jordan M. Grider, Soundharya Dhanabal, Tarikul I. Milon, Feng Chen, Wu Xu

“…A project-oriented learning approach based on the Triangular Spatial Relationship (TSR) algorithm has been developed. The TSR-based method was designed for protein 3D structural comparison, motif discovery and probing molecular interactions. …”
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Epilepsy Diagnosis Analysis via a Multiple-Measures Composite Strategy from the Viewpoint of Associated Network Analysis Methods by Haoying Niu, Tiange Mu, Yuting Wang, Jiayang Huang, Jie Liu

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Advancing AI in Higher Education: A Comparative Study of Large Language Model-Based Agents for Exam Question Generation, Improvement, and Evaluation by Vlatko Nikolovski, Dimitar Trajanov, Ivan Chorbev

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Identifying the Latitude and Longitude of ATMs in ATM Networks by Niloofar Haghjoo, Mohammad rahmati, Ali Zare Mirakabad

“…The use of these methods has less processing cost and higher speed due to the use of algorithms and graph-based databases, and they provide more accurate results. …”
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DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks by Wasim Khan, Mohammad Haroon, Ahmad Neyaz Khan, Mohammad Kamrul Hasan, Asif Khan, Umi Asma Mokhtar, Shayla Islam

“…As a result, decoders can make graphs that are more like the original graph. Each input data point is represented by a low-dimensional representation and a probability of reconstruction by the algorithm. …”
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Texas rural land market integration: A causal analysis using machine learning applications by Tian Su, Senarath Dharmasena, David Leatham, Charles Gilliland

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Dynamic Identification of Critical Transmission Lines in Power Systems With Wind Power Integration Based on Maximum Influence Theory by Gang Ruan, Changzheng Shao, Hao Wang, Yingxu Jin, Lingzi Zhu, Dongxu Chang

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