scholarly journals Classification of Arabic fricative consonants according to their places of articulation

2022 ◽  
Vol 12 (1) ◽  
pp. 936
Author(s):  
Youssef Elfahm ◽  
Nesrine Abajaddi ◽  
Badia Mounir ◽  
Laila Elmaazouzi ◽  
Ilham Mounir ◽  
...  
Keyword(s):  
Energy Distribution ◽  
Voice Recognition ◽  
Point Of View ◽  
Support Vector ◽  
Acoustic Cues ◽  
Classification Rate ◽  
Practical Point ◽  
Vector Machines ◽  
Speech Identification

<span>Many technology systems have used voice recognition applications to transcribe a speaker’s speech into text that can be used by these systems. One of the most complex tasks in speech identification is to know, which acoustic cues will be used to classify sounds. This study presents an approach for characterizing Arabic fricative consonants in two groups (sibilant and non-sibilant). From an acoustic point of view, our approach is based on the analysis of the energy distribution, in frequency bands, in a syllable of the consonant-vowel type. From a practical point of view, our technique has been implemented, in the MATLAB software, and tested on a corpus built in our laboratory. The results obtained show that the percentage energy distribution in a speech signal is a very powerful parameter in the classification of Arabic fricatives. We obtained an accuracy of 92% for non-sibilant consonants /f, χ, ɣ, ʕ, ћ, and h/, 84% for sibilants /s, sҁ, z, Ӡ and ∫/, and 89% for the whole classification rate. In comparison to other algorithms based on neural networks and support vector machines (SVM), our classification system was able to provide a higher classification rate.</span>

Flaw Classification of Flexible Copper Clad-Laminated Sheet

2012 ◽  
Vol 579 ◽  
pp. 52-59
Author(s):  
Yih Chih Chiou ◽  
Yu Teng Liang
Keyword(s):  
Error Rate ◽  
Copper Foil ◽  
Classification Error ◽  
Support Vector ◽  
Classification Rate ◽  
Classification Error Rate ◽  
Vector Machines ◽  
Flaw Classification ◽  
Misclassification Rates

This study investigated the classification error rate of eleven flaws commonly occurred in copper foil. The goal was to online identify the type of the flaw being discovered in order to trace the source of the flaw and act correspondingly. The misclassification rates of four popular classifiers were investigated and compared. The results indicated that the best classification rate can be obtained by choosing Support Vector Machines as the classifier and employing all the ten features. The resulting low classification error rate of 4.41% proved the effectiveness of the derived classifier as well as the suitability of the chosen features.

Detection of Amaranthus palmeri sp. Seedlings in Vegetable Farms Using Genetic Algorithm Optimized Support Vector Machine

2014 ◽  
Vol 666 ◽  
pp. 267-271 ◽  
Author(s):  
W.K Wong ◽  
Muralindran Mariappan ◽  
Ali Chekima ◽  
Manimehala Nadarajan ◽  
Brendan Khoo
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Fine Tuning ◽  
Support Vector ◽  
Amaranthus Palmeri ◽  
Weed Species ◽  
Classification Rate ◽  
Vector Machines

This research is a part of a larger research scope to recognise individual weed species for weed scouting and spot weeding. Support Vector Machines are used to classify the presence of specified weeds(Amaranthus palmeri )by analysing the shape of the weeds. Weed leaves are extracted using image dilation and erosion methods. Several shape feature types were proposed and a total of 59 features were used as the feature pool. The feature selection and fine tuning of the Support Vector Machine are performed using Genetic Algorithm. The outcome is a generalised classifier that enables classification of weed leaves with an average of 90.5% classification rate.

scholarly journals Classification of hyperspectral endocrine tissue images using support vector machines

2020 ◽  
Vol 16 (5) ◽  
pp. 1-10
Author(s):  
Marianne Maktabi ◽  
Hannes Köhler ◽  
Magarita Ivanova ◽  
Thomas Neumuth ◽  
Nada Rayes ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Endocrine Tissue ◽  
Vector Machines

scholarly journals Support vector machines for classification of input vectors with different metrics

2011 ◽  
Vol 61 (9) ◽  
pp. 2874-2878 ◽  
Author(s):  
L. Gonzalez-Abril ◽  
F. Velasco ◽  
J.A. Ortega ◽  
L. Franco
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Vector Machines

A Hybrid Methodology For On-Line Process Monitoring

2004 ◽  
Vol 2 (1) ◽  
Author(s):  
Rakesh Kumar ◽  
Avinash M. Jade ◽  
Valadi K. Jayaraman ◽  
Bhaskar D. Kulkarni
Keyword(s):  
Process Monitoring ◽  
Current Practice ◽  
Locally Linear Embedding ◽  
Support Vector ◽  
Nonlinear Dimensionality Reduction ◽  
Hybrid Strategy ◽  
Vector Machines ◽  
On Line ◽  
Line Process

A hybrid strategy of using (i) locally linear embedding for nonlinear dimensionality reduction of high dimensional data and (ii) support vector machines for classification of the resultant features is proposed as a robust methodology for process monitoring. Illustrative examples substantiate the methodology vis-à-vis current practice.

scholarly journals Detection and Classification of Organophosphate Nerve Agent Simulants Using Support Vector Machines with Multiarray Sensors

2004 ◽  
Vol 44 (2) ◽  
pp. 499-507 ◽  
Author(s):  
Omowunmi Sadik ◽  
Walker H. Land, ◽  
Adam K. Wanekaya ◽  
Michiko Uematsu ◽  
Mark J. Embrechts ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Nerve Agent ◽  
Support Vector ◽  
Vector Machines
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