Design of an improved graph-based model for real-time anomaly detection in healthcare using hybrid CNN-LSTM and federated learning by G Muni Nagamani, Chanumolu Kiran Kumar

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A Convolutional Neural Network for Coastal Classification Based on ALOS and NOAA Satellite Data by Kinh Bac Dang, Van Bao Dang, Quang Thanh Bui, Van Vuong Nguyen, Thi Phuong Nga Pham, Van Liem Ngo

“…Therefore, the authors proposed the use of a convolutional neural network (ConvNet) for coastal classification based on these technologies and geomorphic profile graphs. The primary input data is digital elevation/depth models obtained from ALOS and NOAA satellite. …”
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Auxo: A Temporal Graph Management System by Wentao Han, Kaiwei Li, Shimin Chen, Wenguang Chen

“…It supports both efficient global and local queries with low space overhead. Auxo organizes temporal graph data in spatio-temporal chunks. …”
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Hyperspectral Anomaly Detection by Spatial–Spectral Fusion Based on Extreme Value-Entropy Band Selection and Cauchy Graph Distance Optimization by Song Zhao, Yali Lv, Wen Zhang, Lijun Wang, Zhiru Yang, Gaofeng Ren, Bin Wang, Xiaobin Zhao, Tongwei Lu, Jiayao Wang, Wei Li

“…Hyperspectral technology for detecting camouflaged targets in complex backgrounds represents a current research hotspot. Hyperspectral data often contain numerous spectral bands, which can lead to data redundancy. …”
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Optimization of data allocation in hierarchical memory for blocked shortest paths algorithms by A. A. Prihozhy

“…This paper is devoted to the reduction of data transfer between the main memory and direct mapped cache for blocked shortest paths algorithms (BSPA), which represent data by a D[M×M] matrix of blocks. …”
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Feature recommendation strategy for graph convolutional network by Jisheng Qin, Xiaoqin Zeng, Shengli Wu, Yang Zou

“…Graph Convolutional Network (GCN) is a new method for extracting, learning, and inferencing graph data that builds an embedded representation of the target node by aggregating information from neighbouring nodes. …”
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Discriminative graph regularized representation learning for recognition. by Jinshan Qi, Rui Xu

“…Feature extraction has been extensively studied in the machine learning field as it plays a critical role in the success of various practical applications. To uncover compact low-dimensional feature representations with strong generalization and discrimination capabilities for recognition tasks, in this paper, we present a novel discriminative graph regularized representation learning (DGRL) model that is able to elegantly incorporate both global and local geometric structures as well as the label structure of data into a joint framework. …”
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GraphFedAI framework for DDoS attack detection in IoT systems using federated learning and graph based artificial intelligence by Mohd Anjum, Ashit Kumar Dutta, Ali Elrashidi, Sana Shahab, Asma Aldrees, Zaffar Ahmed Shaikh, Abeer Aljohani

“…Federated learning is incorporated to maintain data privacy by training models locally on each device without sharing raw data. …”
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A Sensor Data Prediction and Early-Warning Method for Coal Mining Faces Based on the MTGNN-Bayesian-IF-DBSCAN Algorithm by Mingyang Liu, Xiaodong Wang, Wei Qiao, Hongbo Shang, Zhenguo Yan, Zhixin Qin

“…The MTGNN (Multi-Task Graph Neural Network) is first employed to model the spatiotemporal coupling characteristics of gas concentration and wind speed data. …”
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CrossGraphNet: a cross-spatiotemporal graph-based method for traffic speed reconstruction using remote sensing vehicle detection by Yan Zhang, Mei-Po Kwan, Jiannan Cai, Jianying Wang, Peifeng Ma

“…The method is designed to address the challenges of traffic modeling in the absence of ground observation data. Combining high-resolution remote sensing imagery, vehicle object detection, and graph modeling technology, our approach could handle the discontinuous spatiotemporal graph information. …”
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Cross-modal adaptive reconstruction of open education resources by Tang Shengju, Feng Li, Zhan Wang, Xie Zhaoyuan

“…The dynamic knowledge graph improved recommendation accuracy by 35.5% while achieving low system latency (1.45 s average, 99% of responses ≤ 1.8 s). …”
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Drug repurposing for Alzheimer’s disease using a graph-of-thoughts based large language model to infer drug-disease relationships in a comprehensive knowledge graph by Zhiping Paul Wang, Xi Li, Nicholas Matsumoto, Mythreye Venkatesan, Jui-Hsuan Chang, Jay Moran, Hyunjun Choi, Binglan Li, Yufei Meng, Miguel E. Hernandez, Jason H. Moore

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Analyzing Position, Velocity and Acceleration Graphs using Arduino by A. Çoban, R. Salar

“…In this study, an Arduino-based in-class physics activity was developed, in which students can analyze position-time, velocity-time and acceleration-time graphs practically. Within the scope of the study, Arduino UNO and HC-SR04 distance sensors, which are very low cost and easily obtainable, were used in the material development process. …”
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Graph theoretical model of a sensorimotor connectome in zebrafish. by Michael Stobb, Joshua M Peterson, Borbala Mazzag, Ethan Gahtan

“…The best quantitative approach to analyzing connectome data is still unclear but graph theory has been used with success. …”
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Structural Fingerprinting of Crystalline Materials from XRD Patterns Using Atomic Cluster Expansion Neural Network and Atomic Cluster Expansion by Xiao Zhang, Xitao Wang, Shunbo Hu

“…This study introduces a novel contrastive learning-based X-ray diffraction (XRD) analysis framework, an SE(3)-equivariant graph neural network (E3NN) based Atomic Cluster Expansion Neural Network (EACNN), which reduces the strong dependency on databases and initial models in traditional methods. …”
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Distinct effects of global signal regression on brain activity during propofol and sevoflurane anesthesia by Fa Lu, Fa Lu, Lunxu Li, Juan Wang, Juan Wang, Xuanling Chen, Ho-Ching Yang, Xiaoli Li, Lan Yao, Zhenhu Liang, Zhenhu Liang

“…IntroductionGlobal signal regression (GSR) is widely used in functional magnetic resonance imaging (fMRI) analysis, yet its effects on anesthetic-related brain activity are not well understood.MethodsUsing fMRI data from patients under general anesthesia, we analyzed temporal variability indices, amplitude of low-frequency fluctuations, functional connectivity, and graph theoretical measures with and without GSR.ResultsHere we show that GSR differentially affects brain activity patterns during propofol- and sevoflurane-induced unconsciousness. …”
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A Dual-Encoder Contrastive Learning Model for Knowledge Tracing by Yanhong Bai, Xingjiao Wu, Tingjiang Wei, Liang He

“…However, existing methods suffer from data sparsity challenges, resulting in inadequate representation quality for low-frequency knowledge concepts and inconsistent modeling of students’ actual knowledge states. …”
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Integrated logistic sigmoid model and graphical analyses of concentration-response relationships of copper sulfate toxicity in aquatic organisms by Djohan Djohan

“…In this study, 10 LSM-based effect selection criteria were developed and used, a set of low-medium-high (LMH) and sigmoid-flat-quadrant (SFQ) graphs were created to evaluate a set of sublethal and lethal CRRs, and the usefulness of LSM-LMH-SFQ in aquatic toxicology was discussed. …”
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Estimation of Arterial Path Flow Considering Flow Distribution Consistency: A Data-Driven Semi-Supervised Method by Zhe Zhang, Qi Cao, Wenxie Lin, Jianhua Song, Weihan Chen, Gang Ren

“…To solve this problem, this paper develops a semi-supervised arterial path flow estimation method considering the consistency of path flow distribution by combining the sparse AVI data and the low permeability CV data. Firstly, this paper proposes a semi-supervised arterial path flow estimation model based on multi-knowledge graphs. …”
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Multi-label feature selection based on dynamic graph Laplacian by Yonghao LI, Liang HU, Ping ZHANG, Wanfu GAO

“…In view of the problems that graph-based multi-label feature selection methods ignore the dynamic change of graph Laplacian matrix, as well as such methods employ logical-value labels to guide feature selection process and loses label information, a multi-label feature selection method based on both dynamic graph Laplacian matrix and real-value labels was proposed.The robust low-dimensional space of feature matrix was used to construct a dynamic graph Laplacian matrix, and the robust low-dimensional space was used as the real-value label space.Furthermore, manifold and non-negative constraints were adopted to transform logical labels into real-valued labels to address the issues mentioned above.The proposed method was compared to three multi-label feature selection methods on nine multi-label benchmark data sets in experiments.The experimental results demonstrate that the proposed multi-label feature selection method can obtain the higher quality feature subset and achieve good classification performance.…”
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