Search Results - (( pattern extraction path algorithm ) OR ( basic visualisation using algorithm ))
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1
Feature extraction: hand shape, hand position and hand trajectory path
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2
Animal voice recognition for identification (ID) detection system
Published 2011“…While the voice pattern classification will be done by using DTW algorithm. …”
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3
Dog voice identification (ID) for detection system
Published 2012“…While the voice pattern classification will be done by using DTW algorithm. …”
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4
NN with DTW-FF Coefficients and Pitch Feature for Speaker Recognition
Published 2006“…This paper proposes a new method to extract speech features in a warping path using dynamic programming (DP). …”
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5
Local DTW coefficients and pitch feature for back-propagation NN digits recognition
Published 2006“…This paper presents a method to extract existing speech features in dynamic time warping path which originally was derived from LPC. …”
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6
Local DTW Coefficients and Pitch Feature for Back-Propagation NN Digits Recognition
Published 2006“…This paper presents a method to extract existing speech features in dynamic time warping path which originally was derived from LPC. …”
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7
Composing multi-relations association rules from crowdsourcing remuneration data
Published 2022“…To gain a more understandable insight, the data relationship is visualised using an alluvial chart that manages to illustrate the flow. …”
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8
Axis manipulation to solve Inverse Kinematics of Hyper-Redundant Robot in 3D Space
Published 2022“…In this study, an axis manipulation using a geometrical approach is used. Initially, a general algorithm for a 2-m-link hyper-redundant robot in 3D is generated. …”
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9
Advances in materials informatics: A review
Published 2024“…Conventional ML models are simple and interpretable, relying on statistical techniques and algorithms to learn patterns and make predictions with limited data. …”
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10
A hybrid environment control system combining EMG and SSVEP signal based on brain-computer interface technology
Published 2021“…The feature in terms of the common spatial pattern (CSP) has been extracted from four classes of SSVEP response, and extracted feature has been classified using K-nearest neighbors (k-NN) based classification algorithm. …”
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