Online modeling method based on dynamic time warping and least squares support vector machine for fermentation process

2010 ◽  
Author(s):  
Gong Yanjie ◽  
Gao Xuejin ◽  
Wang Pu ◽  
Qi Yongsheng
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Dynamic Time Warping ◽  
Fermentation Process ◽  
Modeling Method ◽  
Support Vector ◽  
Time Warping ◽  
Online Modeling ◽  
Dynamic Time

scholarly journals Development of Smart Healthcare System Based on Speech Recognition Using Support Vector Machine and Dynamic Time Warping

2020 ◽  
Vol 12 (6) ◽  
pp. 2403 ◽  
Author(s):  
Ahmed Ismail ◽  
Samir Abdlerazek ◽  
Ibrahim M. El-Henawy
Keyword(s):  
Support Vector Machine ◽  
Speech Recognition ◽  
Elderly People ◽  
Dynamic Time Warping ◽  
Smart Devices ◽  
Support Vector ◽  
Time Warping ◽  
Home Appliances ◽  
Iot Devices ◽  
Dynamic Time

This paper presents an effective solution based on speech recognition to provide elderly people, patients and disabled people with an easy control system. The goal is to build a low-cost system based on speech recognition to easily access Internet of Things (IoT) devices installed in smart homes and hospitals without relying on a centralized supervisory system. The proposed system used a Raspberry Pi board to control home appliances through wireless with smartphones. The main purpose of this system is to facilitate interactions between the user and home appliances through IoT communications based on speech commands. The proposed framework contribution uses a hybrid Support Vector Machine (SVM) with a Dynamic Time Warping (DTW) algorithm to enhance the speech recognition process. The proposed solution is a machine learning-based system for controlling smart devices through speech commands with an accuracy of 97%. The results helped patients and elderly people to access and control IoT devices that are compatible with our system using speech recognition. The proposed speech recognition system is flexible with scalability and availability in adapting to existing smart IoT devices, and it provides privacy in managing patient devices. The research provides an effective method to integrate our systems among medical institutions to help elderly people and patients.

scholarly journals Peranan Kontur dan Slope dalam Pengenalan Keaslian Tanda Tangan Menggunakan Dynamic Time Warping dan Polar Fourier Transform

2016 ◽  
Vol 12 (2) ◽  
Author(s):  
Ignatia Dhian Estu Karisma Ratri ◽  
Hanung Adi Nugroho ◽  
Teguh Bharata Adji
Keyword(s):  
Support Vector Machine ◽  
Fourier Transform ◽  
Dynamic Time Warping ◽  
Combination Method ◽  
Support Vector ◽  
Time Warping ◽  
Svm Classification ◽  
Signature Recognition ◽  
Dynamic Time ◽  
Near Future

The writer has seen that so far signatures are just validated manually, so there is possibility to create a system for hand signature recognition.  The objective of this research is to improve the method for hand signature recognition using combination method with different characteristic. Contour and slope used for special feature in this research. Contour and slope from image will be applied using Dynamic Time Warping (DTW). Another extraction feature that been used was Polar Fourier Transform (PFT).   The method employed for classification are Support Vector Machine (SVM).From the research results, the writer obtains the fact that the combination between the DTW and PFT using SVM classification, provide the better results in verification of an authentic hand signature with the accuracy of 93.23%.  it is expected that from this research, the results can be utilized in the process of verification of an authentic hand signature in near future dailylife.

scholarly journals An empirical evaluation of kernels for time series

2021 ◽  
Author(s):  
Mourtadha Badiane ◽  
Pádraig Cunningham
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Dynamic Time Warping ◽  
Computational Cost ◽  
Empirical Evaluation ◽  
Distance Measures ◽  
Support Vector ◽  
Surprising Result ◽  
Time Warping ◽  
Dynamic Time

AbstractThere exist a variety of distance measures which operate on time series kernels. The objective of this article is to compare those distance measures in a support vector machine setting. A support vector machine is a state-of-the-art classifier for static (non-time series) datasets and usually outperforms k-Nearest Neighbour, however it is often noted that that 1-NN DTW is a robust baseline for time-series classification. Through a collection of experiments we determine that the most effective distance measure is Dynamic Time Warping and the most effective classifier is kNN. However, a surprising result is that the pairing of kNN and DTW is not the most effective model. Instead we have discovered via experimentation that Dynamic Time Warping paired with the Gaussian Support Vector Machine is the most accurate time series classifier. Finally, with good reason we recommend a slightly inferior (in terms of accuracy) model Time Warp Edit Distance paired with the Gaussian Support Vector Machine as it has a better theoretical basis. We also discuss the reduction in computational cost achieved by using a Support Vector Machine, finding that the Negative Kernel paired with the Dynamic Time Warping distance produces the greatest reduction in computational cost.

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