确定方向
发表于 2025-3-26 22:17:07
Track-Based Forecasting of Pedestrian Behavior by Polynomial Approximation and Multilayer Perceptroion errors for starting scenes and 32 % for stopping scenes in comparison to applying a constant velocity movement model. Approaches based on MLP without polynomial input or Support Vector Regression (SVR) models as motion predictor are outperformed as well.
boisterous
发表于 2025-3-27 02:56:04
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BOAST
发表于 2025-3-27 08:00:20
1860-949X liSys 2015 held November 10-11, 2015 in London, UK.Focuses o.This book is a remarkable collection of chapters covering a wider range of topics, including unsupervised text mining, anomaly and Intrusion Detection, Self-reconfiguring Robotics, application of Fuzzy Logic to development aid, Design and
PUT
发表于 2025-3-27 10:38:43
https://doi.org/10.1007/978-3-319-33386-1Artificial Intelligence; Computational Intelligence; Intelligent Systems; Intellisys2015; SAI
最初
发表于 2025-3-27 15:02:40
Yaxin Bi,Supriya Kapoor,Rahul BhatiaPresents recent research in Intelligent Systems and Applications.Carefully selected papers from the SAI Intelligent Systems Conference IntelliSys 2015 held November 10-11, 2015 in London, UK.Focuses o
innovation
发表于 2025-3-27 19:55:53
Studies in Computational Intelligencehttp://image.papertrans.cn/i/image/470031.jpg
有帮助
发表于 2025-3-27 23:19:36
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Mawkish
发表于 2025-3-28 05:58:15
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CRUE
发表于 2025-3-28 09:36:54
A Hybrid Intelligent Approach for Metal-Loss Defect Depth Prediction in Oil and Gas Pipelines,ediction, and defect severity level determination. In this paper, we propose an intelligent system approach for defect prediction in oil and gas pipelines. The proposed technique is based on the . (MFL) technology widely used in pipeline monitoring systems. In the first stage, the MFL signals are an
欢乐中国
发表于 2025-3-28 12:31:23
Predicting Financial Time Series Data Using Hybrid Model,dynamic nature. Support vector regression (SVR), Support vector machine (SVM) and back propagation neural network (BPNN) are the most popular data mining techniques in prediction financial time series. In this paper a hybrid combination model is introduced to combine the three models and to be most