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Titlebook: Intelligent Systems and Applications; Extended and Selecte Yaxin Bi,Supriya Kapoor,Rahul Bhatia Book 2016 Springer International Publishing

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樓主: Enlightening
31#
發(fā)表于 2025-3-26 22:17:07 | 只看該作者
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.
32#
發(fā)表于 2025-3-27 02:56:04 | 只看該作者
33#
發(fā)表于 2025-3-27 08:00:20 | 只看該作者
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
34#
發(fā)表于 2025-3-27 10:38:43 | 只看該作者
https://doi.org/10.1007/978-3-319-33386-1Artificial Intelligence; Computational Intelligence; Intelligent Systems; Intellisys2015; SAI
35#
發(fā)表于 2025-3-27 15:02:40 | 只看該作者
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
36#
發(fā)表于 2025-3-27 19:55:53 | 只看該作者
Studies in Computational Intelligencehttp://image.papertrans.cn/i/image/470031.jpg
37#
發(fā)表于 2025-3-27 23:19:36 | 只看該作者
38#
發(fā)表于 2025-3-28 05:58:15 | 只看該作者
39#
發(fā)表于 2025-3-28 09:36:54 | 只看該作者
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
40#
發(fā)表于 2025-3-28 12:31:23 | 只看該作者
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
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