Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio Optimization by Shiguo Huang, Linyu Cao, Ruili Sun, Tiefeng Ma, Shuangzhe Liu

“…In the first stage, we develop a stock trend prediction model for stock pre-selection called the AGC-CNN model, which leverages a convolutional neural network (CNN), self-attention mechanism, Graph Convolutional Network (GCN), and k-reciprocal nearest neighbors (k-reciprocal NN). …”
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Accelerating Streaming Subgraph Matching via Vector Databases by Liuyi Chen, Yi Ding, Xushuo Tang, Fangyue Chen, Siyuan Gong, Xu Zhou, Zhengyi Yang

“…Graphs are widely used in applications such as social network analysis, bioinformatics, and recommendation systems to represent relationships and complex dependencies. …”
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GT-SRR: A Structured Method for Social Relation Recognition with GGNN-Based Transformer by Dejiao Huang, Menglei Xia, Ruyi Chang, Xiaohan Kong, Shuai Guo

“…In order to overcome these restrictions, this essay suggests a SRR model that integrates Gated Graph Neural Network (GGNN) and Transformer. The task for SRR in this model is image-based. …”
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Robot Visual Tracking Model Based on Improved GOTURN-LD Algorithm by Lijuan Xu, Dalong Liu, Huanjian Ma

“…In the precision graph index score, the average score of the research model was 0.79 and the curves were all in the outermost circle. …”
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Data poisoning attack detection approach for quality of service aware cloud API recommender system by Zhen CHEN, Wenchao QI, Taiyu BAO, Limin SHEN

“…To solve the problem that existing studies usually assumed that the QoS data of cloud API recommender system was reliable, ignoring the data poisoning attack on cloud API recommender system by malicious users in open network environment, a data poisoning attack detection approach based on multi-feature fusion was proposed.Firstly, a user connected network graph was constructed based on the designed similarity function, and users’ neighborhood features were captured using Node2vec.Secondly, sparse auto-encoder was used to mine user QoS deep feature, and user interpretation feature based on QoS data weighted average deviation was designed.Furthermore, a fake user detection model based on support vector machine was established by integrating user neighborhood feature, QoS deep feature, and interpretation feature, the model parameters were learned using grid search and alternating iterative optimization strategy to complete fake user detection.Finally, the effectiveness and superiority of the proposed approach were verified through extensive experiments, realizing the poison attack defense against QoS aware cloud API recommender system at the data side.…”
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A proof-of-concept methodology for identifying topical scientific issues in new publications whose citations have not yet been established by B. N. Chigarev

“…In order to identify the terms that characterize relevant research topics, it is proposed to represent the term co-occurrence network in coordinates of the average occurrence of the term per year and the average normalized citation of the term to visualize the graph. …”
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The application of suitable sports games for junior high school students based on deep learning and artificial intelligence by Xueyan Ji, Shamsulariffin Bin Samsudin, Muhammad Zarif Bin Hassan, Noor Hamzani Farizan, Yubin Yuan, Wang Chen

“…This study intends to develop a Spatial Temporal-Graph Convolutional Network (ST-GCN) action detection algorithm based on the MediaPipe framework. …”
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Activity dependent degeneration explains hub vulnerability in Alzheimer's disease. by Willem de Haan, Katherine Mott, Elisabeth C W van Straaten, Philip Scheltens, Cornelis J Stam

“…Resulting structural and functional network changes were assessed with graph theoretical analysis. …”
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Application of Improved Image Processing Technology in New Media Short Video Quality Improvement in Film and Television Postproduction by Yi Wang

“…The average SSIM of the algorithm in this paper is 0.903, which is closer to 1. …”
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Friend Link Prediction Method Based on Heterogeneous Multigraph and Hierarchical Attention by Aoxue Liu, Boyu Li, Yong Wang, Ziteng Yang

“…Predicting potential friendships accurately from abundant information has become a pivotal research area. While graph neural network (GNN) have shown significant promise in prediction, existing approaches often fail to fully exploit the heterogeneous data characteristics in LBSN. …”
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An urban road traffic flow prediction method based on multi-information fusion by Xiao Wu, Hua Huang, Tong Zhou, Yudan Tian, Shisen Wang, Jingting Wang

“…However, most of the existing studies have used historical data to predict future traffic flows for short periods of time. Spatio-Temporal Graph Neural Networks (STGNN) solves the problem of combining temporal properties and spatial dependence, but does not extract long-term trends and cyclical features of historical data. …”
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Triple-E Principle: Leveraging Occam’s Razor for Dance Energy Expenditure Estimation by Kuan Tao, Kun Meng, Bingcan Gao, Junchao Yang, Junqiang Qiu

“…Notably, wrist accelerometers and heart rate alone provided sufficient accuracy (RMSE: 0.35-0.36), highlighting a trade-off between Effectiveness and Efficiency. A deep-learning network pipeline based on the Extension principle automatically extracted features, achieving an average RMSE to 0.15. …”
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Edge Cloud Resource Scheduling with Deep Reinforcement Learning by Y. Feng, M. Li, J. Li, Y. Yu

“…We utilize a transformer architecture to capture resource states on directed acyclic graphs (DAGs), accelerating the aggregation speed of the Graph Neural Network (GNN). …”
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Artificial Intelligence–Aided Diagnosis System for the Detection and Classification of Private-Part Skin Diseases: Decision Analytical Modeling Study by Wei Wang, Xiang Chen, Licong Xu, Kai Huang, Shuang Zhao, Yong Wang

“…In the second stage, we proposed a knowledge graph based on dermatology expertise and constructed a decision network to classify seven PPSDs (condyloma acuminatum, Paget disease, eczema, pearly penile papules, genital herpes, syphilis, and Bowen disease). …”
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DCASAM: advancing aspect-based sentiment analysis through a deep context-aware sentiment analysis model by Xiangkui Jiang, Binglong Ren, Qing Wu, Wuwei Wang, Hong Li

“…This model integrates the capabilities of Deep Bidirectional Long Short-Term Memory Network (DBiLSTM) and Densely Connected Graph Convolutional Network (DGCN), enhancing the ability to capture long-distance dependencies and subtle contextual variations.The DBiLSTM component effectively captures sequential dependencies, while the DGCN component leverages densely connected structures to model intricate relationships within the data. …”
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Inheritance and protection of intangible cultural heritage in drama category based on AI human–computer interaction and digital technology by Jingjing Ren

“…The model uses a Higher-Order Graph Neural Network (HOGNN) combined with an attention mechanism and Low-Rank Adaptation (LoRA) algorithm to construct a dynamic relational network. …”
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Simultaneous Estimation of Wrist Joint Angle and Torque During Isokinetic Contraction Based on HD-sEMG by Mingjie Yan, Zhe Chen, Jianmin Li, Jinhua Li, Lizhi Pan

“…Six other decoding models were also used to continuously estimate the wrist joint angle and torque, including support vector machine (SVM), residual network (ResNet), long short-term memory (LSTM), transformer-based model (TBM), muscle synergy-based graph attention networks (MSGAT-LSTM), and spatio-temporal feature extraction network (STFEN). …”
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Unveiling fine-scale spatial structures and amplifying gene expression signals in ultra-large ST slices with HERGAST by Yuqiao Gong, Xin Yuan, Qiong Jiao, Zhangsheng Yu

“…To tackle the potential over-smoothing problem arising from data splitting, we construct a heterogeneous graph network to incorporate both local and global spatial relationships. …”
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Resting state connectivity biomarkers of seizure freedom after epilepsy surgery by Eva Martinez-Lizana, Armin Brandt, Matthias Dümpelmann, Andreas Schulze-Bonhage

“…Alterations in brain networks may cause the lowering of the seizure threshold and hypersynchronization that underlie the recurrence of unprovoked seizures in epilepsy. …”
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ADHD detection from EEG signals using GCN based on multi-domain features by Ling Li, Xueyang Guo, Zihan Yang, Yanping Zhao, Xu Liu, Junxian Yang, Yanyan Chen, Xinxian Peng, Lina Han

“…While many researchers have explored automated ADHD detection methods, developing accurate, rapid, and reliable approaches remains challenging.MethodsThis study proposes a graph convolutional neural network (GCN)-based ADHD detection framework utilizing multi-domain electroencephalogram (EEG) features. …”
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