Titlebook: Communications, Networking, and Information Systems; First International Haonan Chen,Pingyi Fan,Lipo Wang Conference proceedings 2023 The

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https://doi.org/10.1007/978-3-319-68879-4m traditional SVR, the enhanced SVR uses the recent data to train the model, rather than all history record. An experiment was performed to compared the proposed SVR with traditional SVR and decision tree algorithms. The RMSE are used as metric. The result shows that the proposed SVR is better than the existing comparing algorithm.

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An Improved LED Aruco-Marker Detection Method for Event Cameraucted in dynamic environments, we have demonstrated the high efficiency and accuracy of this method. In the future, we aim to develop a new fiducial marker that is even faster, more precise, and more suitable for robot vision tasks.

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Stock Trend Prediction Based on Improved SVRm traditional SVR, the enhanced SVR uses the recent data to train the model, rather than all history record. An experiment was performed to compared the proposed SVR with traditional SVR and decision tree algorithms. The RMSE are used as metric. The result shows that the proposed SVR is better than the existing comparing algorithm.

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Improvement of CenterNet Based on Feature Pyramid Networksructure, which effectively improves the accuracy of small-scale object detection. The experimental results show that the detection accuracy of the improved algorithm is improved from 30.1 AP to 30.6 AP for all scale objects on coco dataset, and 3.5 AP for small scale objects.

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1865-0929 , CNIS 2023, held in Guilin, China, in March 2023. .The 8 full papers and 2 short papers presented were thoroughly reviewed and selected from the 32 submissions. The papers are organized in topical sections on ​communications and networking, and information systems and artificial intelligence..978-9

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Solar Radiation and Photovoltaic Conversion,ructure, which effectively improves the accuracy of small-scale object detection. The experimental results show that the detection accuracy of the improved algorithm is improved from 30.1 AP to 30.6 AP for all scale objects on coco dataset, and 3.5 AP for small scale objects.