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

Henni Sid Ahmed
Henni Sid Ahmed
 e 
Jean Caelen
Jean Caelen
   | 30 dic 2020
International Journal on Smart Sensing and Intelligent Systems's Cover Image
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Article Category: Research-Article
Pubblicato online: 30 dic 2020
Pagine: 1 - 28
Ricevuto: 29 set 2020
DOI: https://doi.org/10.21307/ijssis-2020-034
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© 2020 Henni Sid Ahmed et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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 classified Precision polynomial kernel (%) Recall for l polynomial kernel (%)
I1 94.78 95.01
I2 74.84 77.71
I3 91.90 93.69

Painting of the recall and precision for the recognition of the words for the polynomial kernel nucleus.

Word classified Precision polynomial kernel (%) Recall for polynomial kernel (%)
W1 89.51 90.84
W2 87.32 89.01
W3 92.50 94.15
W4 83.11 84.71
W5 53.11 61.21
W6 49.06 68.25
W7 79.62 84.14
W8 56.01 55.19
W9 76.48 82.91
W10 74.02 79.18
W11 78.70 87.02
W12 67.92 76.21
W13 74.52 80.56
W14 71.93 76.63
W15 76.49 84.26
W16 65.03 59.19
W17 88.01 90.12
W18 86.12 89.03
W19 90.56 93.89
W20 80.48 84.08

Painting of the recall and precision for the recognition of the image for the RBF kernel nucleus.

Image classified Precision RBF kernel Recall for RBF kernel
I1 96.40 96.99
I2 76.98 79.89
I3 93.59 95.24

Painting of the recall and precision for the recognition of the words for the sigmoid kernel nucleus.

Word classified Precision for sigmoid kernel (%) Recall for sigmoid kernel (%)
W1 88.70 90.21
W2 86.51 88.27
W3 91.75 93.39
W4 81.60 83.90
W5 52.36 60.39
W6 48.32 67.40
W7 78.83 83.31
W8 55.32 54.30
W9 75.72 82.01
W10 73.34 78.35
W11 77.90 86.28
W12 67.08 75.39
W13 73.72 79.69
W14 71.07 75.86
W15 75.75 83.39
W16 64.32 58.40
W17 87.28 89.27
W18 85.29 88.25
W19 89.72 93.07
W20 79.75 83.24

Painting of the recall and precision for the detection of linear behavior for the core.

Image classified Precision linear kernel (%) Recall for linear kernel (%)
I1 94.37 94.41
I2 74.39 77.28
I3 91.40 93.04

Painting of the recall and precision for the recognition of the image for the sigmoid kernel nucleus.

Image classified Precision linear kernel (%) Recall for linear kernel (%)
I1 93.97 94.02
I2 73.05 77.05
I3 91.11 92.89

Painting of the recall and precision for the recognition of the words for the linear kernel nucleus.

Word classified Precision linear kernel Recall for linear kernel
W1 90.36 91.75
W2 88.63 89.93
W3 93.75 95.13
W4 83.47 85.59
W5 54.23 62.19
W6 50.17 69.21
W7 81.01 85.01
W8 57.21 56.17
W9 77.69 83.65
W10 75.11 80.09
W11 79.63 87.67
W12 68.82 77.18
W13 75.56 81.45
W14 72.86 77.63
W15 77.32 85.17
W16 65.83 60.14
W17 88.93 91.04
W18 87.01 90.01
W19 91.72 94.72
W20 81.31 85.03

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

Word classified Precision for RBF kernel (%) Recall for RBF kernel (%)
W1 91.59 93.31
W2 90.17 91.39
W3 96.21 96.59
W4 85.76 88.13
W5 56.11 63.69
W6 52.56 70.51
W7 82.45 86.59
W8 59.32 57.72
W9 82.51 86.17
W10 80.23 82.89
W11 84.17 89.52
W12 70.46 79.31
W13 78.14 84.21
W14 74.86 80.14
W15 79.53 87.59
W16 67.54 62.45
W17 89.69 92.45
W18 87.79 90.13
W19 93.72 98.14
W20 84.09 87.75

Painting of the recall and precision for the recognition of the image for the linear kernel nucleus.

Image classified Precision linear kernel (%) Recall for linear kernel (%)
I1 95.42 95.96
I2 75.49 78.29
I3 92.47 94.28
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