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The command of comfort in an intelligent building by speech classification and image classification for energy optimization

Open Access
|Dec 2020

Figures & Tables

Figure 1:

The diagram of the automatic speech recognition system by MFCC and SVM.
The diagram of the automatic speech recognition system by MFCC and SVM.

Figure 2:

The rate of the recognition of the words w1 to w10 by Mel FCC-SVM using the linear kernel nucleus.
The rate of the recognition of the words w1 to w10 by Mel FCC-SVM using the linear kernel nucleus.

Figure 3:

The rate of the recognition of the words w11 to w20 by Mel FCC-SVM using the linear kernel nucleus.
The rate of the recognition of the words w11 to w20 by Mel FCC-SVM using the linear kernel nucleus.

Figure 4:

The rate of the recognition of the words w1 to w10 by Mel FCC-SVM using the RBF (radial basic function) nucleus.
The rate of the recognition of the words w1 to w10 by Mel FCC-SVM using the RBF (radial basic function) nucleus.

Figure 5:

The rate of the recognition of the words w11 to w20 by Mel FCC-SVM using the RBF (radial basic function) nucleus.
The rate of the recognition of the words w11 to w20 by Mel FCC-SVM using the RBF (radial basic function) nucleus.

Figure 6:

The rate of the recognition of the words w1 to w10 by Mel FCC-SVM using the polynomial kernel nucleus.
The rate of the recognition of the words w1 to w10 by Mel FCC-SVM using the polynomial kernel nucleus.

Figure 7:

The rate of the recognition of the words w11 to w20 by Mel FCC-SVM using the polynomial kernel nucleus.
The rate of the recognition of the words w11 to w20 by Mel FCC-SVM using the polynomial kernel nucleus.

Figure 8:

The rate of the recognition of the words w1 to w10 by Mel FCC-SVM using the sigmoid nucleus.
The rate of the recognition of the words w1 to w10 by Mel FCC-SVM using the sigmoid nucleus.

Figure 9:

The rate of the recognition of the words w11 to w20 by Mel FCC-SVM using the sigmoid nucleus.
The rate of the recognition of the words w11 to w20 by Mel FCC-SVM using the sigmoid nucleus.

Figure 10:

Photos of the Domus intelligent building layout, I1: the person is present, I2: the person is asleep, I3: the person is absent.
Photos of the Domus intelligent building layout, I1: the person is present, I2: the person is asleep, I3: the person is absent.

Figure 11:

The diagram of the image classification system by SIFT and SVM.
The diagram of the image classification system by SIFT and SVM.

Figure 12:

The rate of the recognition of the images by SIFT-SVM using the linear kernel nucleus.
The rate of the recognition of the images by SIFT-SVM using the linear kernel nucleus.

Figure 13:

The rate of the recognition of the images by SIFT-SVM using the RBF (radial basic function) nucleus.
The rate of the recognition of the images by SIFT-SVM using the RBF (radial basic function) nucleus.

Figure 14:

The rate of the recognition of the images by SIFT-SVM using the polynomial kernel nucleus.
The rate of the recognition of the images by SIFT-SVM using the polynomial kernel nucleus.

Figure 15:

The rate of the recognition of the images by SIFT-SVM using the sigmoid nucleus.
The rate of the recognition of the images by SIFT-SVM using the sigmoid nucleus.

Figure 16:

Diagram of the system of the command of comfort in the intelligent building.
Diagram of the system of the command of comfort in the intelligent building.

Figure 17:

The rate of the recognition of the words w1 to w10 by Linear PC-SVM using the linear kernel nucleus.
The rate of the recognition of the words w1 to w10 by Linear PC-SVM using the linear kernel nucleus.

Figure 18:

The rate of the recognition of the words w11 to w20 by LPC-SVM using the linear kernel nucleus.
The rate of the recognition of the words w11 to w20 by LPC-SVM using the linear kernel nucleus.

Figure 19:

The rate of the recognition of the words w1 to w10 by LPC-SVM using the RBF (radial basic function) nucleus.
The rate of the recognition of the words w1 to w10 by LPC-SVM using the RBF (radial basic function) nucleus.

Figure 20:

The rate of the recognition of the words w11 to w20 by LPC-SVM using the RBF (radial basic function) nucleus.
The rate of the recognition of the words w11 to w20 by LPC-SVM using the RBF (radial basic function) nucleus.

Figure 21:

The rate of the recognition of the words w1 to w10 by LPC-SVM using the polynomial kernel nucleus.
The rate of the recognition of the words w1 to w10 by LPC-SVM using the polynomial kernel nucleus.

Figure 22:

The rate of the recognition of the words w11 to w20 by LPC-SVM using the polynomial kernel nucleus.
The rate of the recognition of the words w11 to w20 by LPC-SVM using the polynomial kernel nucleus.

Figure 23:

The rate of the recognition of the words w1 to w10 by LPC-SVM using the sigmoid nucleus.
The rate of the recognition of the words w1 to w10 by LPC-SVM using the sigmoid nucleus.

Figure 24:

The rate of the recognition of the words w11 to w20 by LPC-SVM using the sigmoid nucleus.
The rate of the recognition of the words w11 to w20 by LPC-SVM using the sigmoid nucleus.

Figure 25:

The rate of the recognition of the images by LBP-SVM using the linear kernel nucleus.
The rate of the recognition of the images by LBP-SVM using the linear kernel nucleus.

Figure 26:

The rate of the recognition of the images by LBP-SVM using the RBF (radial basic function) nucleus.
The rate of the recognition of the images by LBP-SVM using the RBF (radial basic function) nucleus.

Figure 27:

The rate of the recognition of the images by LBP-SVM using the polynomial kernel nucleus.
The rate of the recognition of the images by LBP-SVM using the polynomial kernel nucleus.

Figure 28:

The rate of the recognition of the images by LBP-SVM using the sigmoid nucleus.
The rate of the recognition of the images by LBP-SVM using the sigmoid nucleus.

Figure 29:

The rate of the recognition of the images by RGB-SVM using the linear kernel nucleus.
The rate of the recognition of the images by RGB-SVM using the linear kernel nucleus.

Figure 30:

The rate of the recognition of the images by RGB-SVM using the RBF (radial basic function) nucleus.
The rate of the recognition of the images by RGB-SVM using the RBF (radial basic function) nucleus.

Figure 31:

The rate of the recognition of the images by RGB-SVM using the polynomial kernel nucleus.
The rate of the recognition of the images by RGB-SVM using the polynomial kernel nucleus.

Figure 32:

The rate of the recognition of the images by RGB-SVM using the sigmoid nucleus.
The rate of the recognition of the images by RGB-SVM using the sigmoid nucleus.

Painting of the recall and precision for the recognition of the image for the polynomial kernel nucleus_

Image classifiedPrecision polynomial kernel (%)Recall for l polynomial kernel (%)
I194.7895.01
I274.8477.71
I391.9093.69

Painting of the recall and precision for the recognition of the words for the polynomial kernel nucleus_

Word classifiedPrecision polynomial kernel (%)Recall for polynomial kernel (%)
W189.5190.84
W287.3289.01
W392.5094.15
W483.1184.71
W553.1161.21
W649.0668.25
W779.6284.14
W856.0155.19
W976.4882.91
W1074.0279.18
W1178.7087.02
W1267.9276.21
W1374.5280.56
W1471.9376.63
W1576.4984.26
W1665.0359.19
W1788.0190.12
W1886.1289.03
W1990.5693.89
W2080.4884.08

Painting of the recall and precision for the recognition of the image for the RBF kernel nucleus_

Image classifiedPrecision RBF kernelRecall for RBF kernel
I196.4096.99
I276.9879.89
I393.5995.24

Painting of the recall and precision for the recognition of the words for the sigmoid kernel nucleus_

Word classifiedPrecision for sigmoid kernel (%)Recall for sigmoid kernel (%)
W188.7090.21
W286.5188.27
W391.7593.39
W481.6083.90
W552.3660.39
W648.3267.40
W778.8383.31
W855.3254.30
W975.7282.01
W1073.3478.35
W1177.9086.28
W1267.0875.39
W1373.7279.69
W1471.0775.86
W1575.7583.39
W1664.3258.40
W1787.2889.27
W1885.2988.25
W1989.7293.07
W2079.7583.24

Painting of the recall and precision for the detection of linear behavior for the core_

Image classifiedPrecision linear kernel (%)Recall for linear kernel (%)
I194.3794.41
I274.3977.28
I391.4093.04

Painting of the recall and precision for the recognition of the image for the sigmoid kernel nucleus_

Image classifiedPrecision linear kernel (%)Recall for linear kernel (%)
I193.9794.02
I273.0577.05
I391.1192.89

Painting of the recall and precision for the recognition of the words for the linear kernel nucleus_

Word classifiedPrecision linear kernelRecall for linear kernel
W190.3691.75
W288.6389.93
W393.7595.13
W483.4785.59
W554.2362.19
W650.1769.21
W781.0185.01
W857.2156.17
W977.6983.65
W1075.1180.09
W1179.6387.67
W1268.8277.18
W1375.5681.45
W1472.8677.63
W1577.3285.17
W1665.8360.14
W1788.9391.04
W1887.0190.01
W1991.7294.72
W2081.3185.03

Painting of the recall and precision for the recognition of the words for the RBF (radial basic function) nucleus_

Word classifiedPrecision for RBF kernel (%)Recall for RBF kernel (%)
W191.5993.31
W290.1791.39
W396.2196.59
W485.7688.13
W556.1163.69
W652.5670.51
W782.4586.59
W859.3257.72
W982.5186.17
W1080.2382.89
W1184.1789.52
W1270.4679.31
W1378.1484.21
W1474.8680.14
W1579.5387.59
W1667.5462.45
W1789.6992.45
W1887.7990.13
W1993.7298.14
W2084.0987.75

Painting of the recall and precision for the recognition of the image for the linear kernel nucleus_

Image classifiedPrecision linear kernel (%)Recall for linear kernel (%)
I195.4295.96
I275.4978.29
I392.4794.28
Language: English
Page range: 1 - 28
Submitted on: Sep 29, 2020
Published on: Dec 30, 2020
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2020 Henni Sid Ahmed, Jean Caelen, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.