A rapid and efficient method for flash flood simulation based on deep learning by Xinying Wang, Miao Xiao, Yi Liu, Jun Guo, Yangyang Qin, Yunkang Zhang

“…Recently, the combination of deep learning methods and hydrodynamic models has shown superior performance in the simulation of urban and plain areas. …”
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Deployment method for vEPC virtualized network function via Q-learning by Quan YUAN, Hong-bo TANG, Kai-zhi HUANG, Xiao-lei WANG, Yu ZHAO

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Deep Learning-Based Layout Analysis Method for Complex Layout Image Elements by Yunfei Zhong, Yumei Pu, Xiaoxuan Li, Wenxuan Zhou, Hongjian He, Yuyang Chen, Lang Zhong, Danfei Liu

“…The method reduces the number of model parameters and training time by replacing the backbone network. …”
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Multi-Task Water Quality Colorimetric Detection Method Based on Deep Learning by Shenlan Zhang, Shaojie Wu, Liqiang Chen, Pengxin Guo, Xincheng Jiang, Hongcheng Pan, Yuhong Li

“…To address this limitation, we propose a multi-task water quality colorimetric detection method based on YOLOv8n, leveraging deep learning techniques to achieve a fully automated process of “image input and result output”. …”
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Plant Classification Method Using Histogram and Machine Learning for Smart Agriculture Applications by Orhan Yaman, Türker Tuncer

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Retracted: Research on Tennis Motion Evaluation Method and System Based on Deep Learning by International Transactions on Electrical Energy Systems

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Access control relationship prediction method based on GNN dual source learning by Dibin SHAN, Xuehui DU, Wenjuan WANG, Aodi LIU, Na WANG

“…With the rapid development and wide application of big data technology, users’ unauthorized access to resources becomes one of the main problems that restrict the secure sharing and controlled access to big data resources.The ReBAC (Relationship-Based Access Control) model uses the relationship between entities to formulate access control rules, which enhances the logical expression of policies and realizes dynamic access control.However, It still faces the problems of missing entity relationship data and complex relationship paths of rules.To overcome these problems, a link prediction model LPMDLG based on GNN dual-source learning was proposed to transform the big data entity-relationship prediction problem into a link prediction problem with directed multiple graphs.A topology learning method based on directed enclosing subgraphs was designed in this modeled.And a directed dual-radius node labeling algorithm was proposed to learn the topological structure features of nodes and subgraphs from entity relationship graphs through three segments, including directed enclosing subgraph extraction, subgraph node labeling calculation and topological structure feature learning.A node embedding feature learning method based on directed neighbor subgraph was proposed, which incorporated elements such as attention coefficients and relationship types, and learned its node embedding features through the sessions of directed neighbor subgraph extraction and node embedding feature learning.A two-source fusion scoring network was designed to jointly calculate the edge scores by topology and node embedding to obtain the link prediction results of entity-relationship graphs.The experiment results of link prediction show that the proposed model obtains better prediction results under the evaluation metrics of AUC-PR, MRR and Hits@N compared with the baseline models such as R-GCN, SEAL, GraIL and TACT.The ablation experiment results illustrate that the model’s dual-source learning scheme outperforms the link prediction effect of a single scheme.The rule matching experiment results verify that the model achieves automatic authorization of some entities and compression of the relational path of rules.The model effectively improves the effect of link prediction and it can meet the demand of big data access control relationship prediction.…”
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Research on intrusion detection method for edge networks based on federated reinforcement learning by DING Kai, HUANG Yidu, TAO Ming, XIE Renping

“…To address this issue, a federated reinforcement learning-based intrusion detection method was proposed, and experiments were conducted using two datasets from the Internet of medical things (IoMT) and Internet of vehicles (IoV) scenarios. …”
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Intelligent guarantee power supply decision method based on reinforcement learning algorithm by Milu Zhou, Huijie Sun, Tian Yang, Tingting Li, Qi Hou

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Retracted: Badminton Backcourt Stroke Route Planning Method Based on Deep Learning by Journal of Mathematics

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Intelligent identification method of origin for Alismatis Rhizoma based on image and machine learning by Wenqi Zhao, Zongyi Zhao, Wen Zheng, Zimin Wang, Gaoting Yang, Zhiqiong Lan, Xiaoli Pan, Min Li

“…Therefore, there is a need for a method that is both fast and objective to determine the source of AR. …”
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Distributed robust scheduling of distribution-microgrid based on deep learning method integration by WANG Yihong, LIU Jichun, QIU Gao, ZHOU Hao, HE Peixin

“…Aiming at the problems such as the uncertainty of distributed power output and the low efficiency of operation in the coupled system scheduling of distribution network and microgrid, an optimized scheduling model of Branch-bar operation with chance constraint based on the integration of deep learning method for distribution network and microgrid interconnection system is proposed. …”
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Logging Prediction Method of Organic Carbon in Mixed Deposits Based on Machine Learning by CHEN Liangyu, HU Lang, XIN Jintao, LI Yonggui, CHEN Zhi, FU Jianwei

“…The machine learning method has a significant advantage in solving the complex nonlinear relationship between data because of its powerful nonlinear mapping ability. …”
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Stroke Risk Classification Using the Ensemble Learning Method of XGBoost and Random Forest by Gullam Almuzadid, Egia Rosi Subhiyakto

“…The proposed approach significantly improved predictive accuracy compared to previous research, demonstrating the effectiveness of ensemble learning and preprocessing methods in developing reliable, high-performing machine learning models for early stroke risk assessment.…”
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Path Planning Method of Mobile Robot Using Improved Deep Reinforcement Learning by Wei Wang, Zhenkui Wu, Huafu Luo, Bin Zhang

“…A mobile robot path planning method based on improved deep reinforcement learning is proposed. …”
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Two-Step Contrast Source Learning Method for Electromagnetic Inverse Scattering Problems by Anran Si, Miao Wang, Fuping Fang, Dahai Dai

“…Due to the intrinsic ill-posedness and nonlinearity of EM-ISPs, traditional non-iterative and iterative methods struggle to meet the requirements of high accuracy and real-time reconstruction. …”
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A Fast Learning Method for Multilayer Perceptrons in Automatic Speech Recognition Systems by Chenghao Cai, Yanyan Xu, Dengfeng Ke, Kaile Su

“…We propose a fast learning method for multilayer perceptrons (MLPs) on large vocabulary continuous speech recognition (LVCSR) tasks. …”
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Equipment Fault Diagnosis Method Based on High-efficient Communication Federated Learning by LIU Jing, ZHAO Yichen, LIU Xinghua, WU Youxi, JI Haipeng

“…However, due to the high heterogeneity of factory equipment operation data, traditional federated learning has low communication efficiency. To address the above issues, an equipment fault diagnosis method based on high-efficiency communication federated learning was proposed. …”
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A Speech Recognition Method Using Competitive and Selective Learning Neural Networks by Hu Guangrui Xu Xiong Yan Yonghong

“…In this paper,a basic principle called the equidistortion principle for vector clustering is theoretically derived by using Gersho’s asymptotic theory,and a new competitive learning algorithm is prorosed with a selection mechanism,called the CLS（Competitive and Selective Learning）algorithm.Because the selection mechanism enables the system to escape from local minima,the proposed algorithm can obtain better performance without a particular initialization procedure.A new neural network algorithm with competitive learning and multiple safe rejection schemes are proposed in the context of parallel,self organizing,hierarchical neural networks（PSHNN）.The input of PSHNN is a subset of the output scores of HMM.The experimental results indicate that the recognition ability of the method based on competitive learning neural network is higher than that of the traditional HMM method.…”
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RETRACTED: Creative thinking method of foreign language communication learning in agrarian university by Bukovsky Stanislav

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