Crack Detection Method of Sleeper Based on Cascade Convolutional Neural Network by Liming Li, Shubin Zheng, Chenxi Wang, Shuguang Zhao, Xiaodong Chai, Lele Peng, Qianqian Tong, Ji Wang

“…The proposed algorithm mainly includes improved You Only Look Once version 3 (YOLOv3) and the crack recognition network, where the crack recognition network includes two modules, the crack encoder-decoder network (CEDNet) and the crack residual refinement network (CRRNet). …”
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Unmanned aerial vehicle–assisted node localization for wireless sensor networks by Xu Yang, Zhenguo Gao, Qiang Niu

“…And, their positions are determined through the graph matching or Bayesian model averaging approach. …”
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Designing Knowledge Map for Knowledge Management projects Using Network Analysis by heidar najafi, mohammad aghdasi, Babak Teimurpoor

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Network analysis of narrative discourse and attention-deficit hyperactivity symptoms in adults. by Rafael Martins Coelho, Cláudia Drummond, Natália Bezerra Mota, Pilar Erthal, Gabriel Bernardes, Gabriel Lima, Raquel Molina, Felipe Kenji Sudo, Rosemary Tannock, Paulo Mattos

“…Speech was recorded and transcribed as an input to SpeechGraphs software. Parameters were total number of words (TNW), number of loops of one node (L1), repeated edges (RE), largest strongly connected component (LSC) and average shortest path (ASP). …”
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Prediction of Electric Vehicle Mileage According to Optimal Energy Consumption Criterion by Oleksii Chkalov, Roman Dropa

“…By representing the road network as a weighted directed graph tailored to the energy consumption model, an algorithm aids in mileage optimization by determining the optimal path for immediate use. …”
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Distributed Downlink Power Control by Message-Passing for Very Large-Scale Networks by Illsoo Sohn

“…Downlink power control is revisited by assuming very large-scale networks. In very large-scale networks, conventional centralized power control schemes quickly become impractical owing to the huge computational burden and limited backhaul capacity. …”
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Distributed Authentication of Power Grid Safety and Stability Control Terminals Based on DHT and Blockchain by Yening LAI, Ke FENG, Tongwei YU, Wang WANG, Guanjun TANG

“…By combining the distributed Hash table (DHT) technology with the blockchain technology, a blockchain distributed storage optimization method is firstly proposed based on DHT of Skip Graph structure. And then a distributed authentication scheme of power grid safety and stability control terminals is designed based on DHT and blockchain technology, and the process and key algorithms for terminal registration, network access and authentication are given. …”
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Leveraging word embeddings to enhance co-occurrence networks: A statistical analysis. by Diego R Amancio, Jeaneth Machicao, Laura V C Quispe

“…Recent studies have explored the addition of virtual edges to word co-occurrence networks using word embeddings to enhance graph representations, particularly for short texts. …”
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Enhancing Risk-Adjusted EWMA Control Chart Utilizing Artificial Neural Networks by Abdullah Ali H. Ahmadini, Imad Khan, Hadeel AlQadi, Saddam Hussain

“…Our proposed approach involves creating an exponentially weighted moving average (EWMA) control chart. This graph is based on residuals derived from the ANN model allowing comprehensive analysis of actual cardiac surgery patient data. …”
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Application of multi-sensor fusion localization algorithm based on recurrent neural networks by Zexia Huang, Guoyang Ye, Pu Yang, Wanshun Yu

“…The method also outperforms state-of-the-art algorithms, including Particle Filter (PF) and Graph SLAM, in both accuracy and computational efficiency, achieving an average runtime of 30.1 ms per frame, suitable for real-time applications. …”
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Research on Associative Cognition Networks with Educational Robotics for Learning Healthy Hydration Habits. by Alejandro De la Hoz Serrano, Lina Viviana Melo Niño, Florentina Cañada Cañada, Javier Cubero Juánez

“…Methodology: The study adopts a mixed approach and graph analysis using Gephi® and Cytoscape® software, and with the use of measures of degree, clustering coefficient, average shortest path length and modularity. …”
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Novel Opportunistic Network Routing Based on Social Rank for Device-to-Device Communication by Tong Wang, Yongzhe Zhou, Yunfeng Wang, Mengbo Tang

“…Meanwhile, in order to select better candidate nodes in the network, a social graph among people is established when they socially relate to each other in social rank replication. …”
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Multi-feature stock price prediction by LSTM networks based on VMD and TMFG by Zhixin Zhang, Qingyang Liu, Yanrong Hu, Hongjiu Liu

“…Finally, the filtered features are modeled and predicted using a Long Short-Term Memory (LSTM) network. Experimental results demonstrate that the VMD–TMFG–LSTM model significantly outperforms AutoRegressive Integrated Moving Average (ARIMA), Neural Network (NN), Deep Neural Network (DNN), Convolutional Neural Network (CNN), as well as single LSTM, TMFG–LSTM, and VMD–LSTM models in forecasting the closing prices of multiple stocks. …”
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Examining perinatal regionalization in practice: a network analysis of maternal transport in Georgia by Jingyu Li, Stephanie M. Radke, Lauren N. Steimle

“…Methods Using birth records in the state of Georgia from 2017 to 2022, we constructed network graphs to represent maternal transport routes among obstetric facilities. …”
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Inter-Satellite Handover Method Based Multi-Objective Optimization in Satellite-Terrestrial Integrated Network by Renpeng LIU, Bo HU, Hequn LI

“…The high-speed motion of low-earth orbit communication satellites results in a highly dynamic network topology, and the spatio-temporal distribution of resources in the satellite-terrestrial integrated network is non-uniform.When multiple users and services switch between satellites, a large number of handover requests are triggered, leading to intensified network resource competition.As a result, the limited satellite resources cannot meet all the handover requests, leading to a significant decrease in handover success rate.In view of the above problem, the multi-objective optimization based satellite handover method was proposed.It introduced the satellite coverage spatio-temporal graph and transforms the dynamic continuous topology into static discrete snapshots, accurately depicted the connections between satellite nodes and users at different times and locations.The multi-objective optimization model was established for satellite handover decisions, and anadaptive accelerated multi-objective evolutionary algorithm(AAMOEA) was proposed to optimized user data rate and network load simultaneously, ensured handover success rate and enhanced network service capability.It built a STIN communication simulation environment and tested the multi user handover performance in a multi satellite overlapping coverage scenario.The results demonstrated that the multi-objective optimization-based satellite handover method achieved an average handover success rate improvement of over 20% compared to benchmark algorithms.…”
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Bluetooth-Based Dynamic Nexus Mesh Communication Network for High-Density Urban Interaction Spaces by Yufei Hu, Ngai Cheong, Muya Yao, Qingwen Long, Yide Yu

“…Traditional centralized network structures exhibit clear scalability and communication efficiency bottlenecks. …”
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PLL-VO： An Efficient and Robust Visual Odometry Integrating Point-Line Features and Neural Networks by L. Zhao, Y. Yang, D. Ma, X. Lin, W. Wang

“…After selecting keyframes based on point feature counts and line feature overlap angles, we integrate convolutional neural networks (CNNs) and graph neural networks (GNNs) to enhance sparse matching, thereby improving both accuracy and computational efficiency. …”
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AI-Driven Framework for Secure and Efficient Load Management in Multi-Station EV Charging Networks by Md Sabbir Hossen, Md Tanjil Sarker, Marran Al Qwaid, Gobbi Ramasamy, Ngu Eng Eng

“…The study also finds important gaps in the current literature and suggests new areas for research, such as using graph neural networks (GNNs) and quantum machine learning to make EV charging infrastructures even more scalable, resilient, and intelligent.…”
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Hyperdimensional computing: a framework for stochastic computation and symbolic AI by Mike Heddes, Igor Nunes, Tony Givargis, Alexandru Nicolau, Alex Veidenbaum

“…Compared to the state-of-the-art Graph Neural Networks, our proposed method achieves comparable accuracy, while training and inference times are on average 14.6× and 2.0× faster, respectively. …”
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A comparative analysis of the most common deterministic methods for the calculation of electricity losses in industrial networks by E. I. Gracheva, Z. M. Shakurova, R. E. Abdullazyanov

“…It is shown that for the method of graphical integration, initial data on the dependences of load schedules for each network element are required, and the method of calculating losses by average node loads can be used in networks with relatively constant loads. …”
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