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

Word classified	Precision linear kernel (%)	Recall for linear kernel (%)
W1	91.01	92.29
W2	89.12	90.59
W3	94.31	95.89
W4	84.09	86.15
W5	54.96	62.96
W6	50.89	69.90
W7	81.63	85.68
W8	57.96	56.85
W9	78.26	84.23
W10	75.85	80.80
W11	80.19	88.42
W12	69.45	77.94
W13	76.13	82.09
W14	73.48	78.29
W15	78.02	85.96
W16	66.51	60.86
W17	89.63	91.79
W18	87.67	90.69
W19	92.03	95.45
W20	82.04	85.78

Table 2.

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	92.12	94.02
W2	90.89	92.05
W3	96.96	97.13
W4	86.42	88.85
W5	56.82	64.33
W6	53.11	71.22
W7	83.08	87.13
W8	60.02	58.45
W9	83.26	86.89
W10	80.96	83.58
W11	84.89	90.22
W12	71.08	80.09
W13	78.86	84.92
W14	75.56	80.85
W15	80.22	88.18
W16	68.23	63.08
W17	90.33	93.11
W18	88.45	90.87
W19	94.36	98.85
W20	84.81	88.58

Table 3.

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	90.20	91.56
W2	88.01	89.74
W3	93.24	94.92
W4	83.11	85.39
W5	53.86	61.99
W6	49.78	68.98
W7	80.33	84.89
W8	56.75	55.93
W9	77.13	83.59
W10	74.71	79.91
W11	79.46	87.75
W12	68.59	76.98
W13	75.22	81.22
W14	72.65	77.36
W15	77.18	84.98
W16	65.76	59.92
W17	88.79	90.84
W18	86.85	89.79
W19	91.26	94.62
W20	81.17	84.81

Table 4.

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	89.40	90.96
W2	87.25	89.01
W3	92.50	94.11
W4	82.35	84.60
W5	53.01	61.14
W6	49.02	68.12
W7	79.50	84.08
W8	56.02	55.09
W9	76.40	82.79
W10	74.03	79.08
W11	78.65	87.01
W12	67.80	76.11
W13	74.40	80.45
W14	71.80	76.60
W15	76.40	84.15
W16	65.01	59.13
W17	88.02	90.02
W18	86.07	89.01
W19	90.45	93.80
W20	80.49	84.01

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

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 w₁₁ to w₂₀ 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 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.

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 w₁₁ to w₂₀ 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 sigmoid nucleus.

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

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

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

Table 5.

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	96.38	96.89
I2	76.42	79.11
I3	93.38	95.16

1 Note: e.2 Classification with SVM using RBF (radial basic function) nucleus.

Table 6.

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	97.29	97.96
I2	77.89	80.65
I3	94.45	96.13

1 Note: e.3 Classification with SVM using the polynomial kernel nucleus.

Table 7.

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	95.56	95.97
I2	75.72	78.56
I3	92.79	94.45

1 Note: e.4 Classification with SVM using the sigmoid nucleus.

Table 8.

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	94.89	94.99
I2	74.91	77.83
I3	91.94	93.66

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 RBF (radial basic function) nucleus.

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 sigmoid nucleus.

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

Table 9.

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

Table 10.

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

Table 11.

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

Table 12.

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

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 w₁₁ to w₂₀ by LPC-SVM using the linear kernel nucleus.

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 w₁₁ to w₂₀ 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 polynomial kernel nucleus.

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 w₁ to w₁₀ 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.

Table 13.

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	95.90	96.41
I2	75.91	78.60
I3	92.80	94.62

1 Note: e.2 Classification with SVM using RBF (radial basic function) nucleus.

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

Table 14.

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	96.70	97.41
I2	77.32	80.08
I3	93.85	95.58

1 Note: e.3 Classification with SVM using the polynomial kernel nucleus.

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

Table 15.

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	95.01	95.38
I2	75.14	78.02
I3	92.19	93.88

1 Note: e.4 Classification with SVM using the sigmoid nucleus.

Table 16.

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

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 sigmoid nucleus.

Table 17.

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

1 Note: e.2 Classification with SVM using RBF (radial basic function) nucleus.

Table 18.

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

1 Note: e.3 Classification with SVM using the polynomial kernel nucleus.

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 RBF (radial basic function) nucleus.

Table 19.

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

1 Note: e.4 Classification with SVM using the sigmoid nucleus.

Table 20.

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

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 sigmoid nucleus.

References

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Articles in this issue

DOI: https://doi.org/10.21307/ijssis-2020-034 | Journal eISSN: 1178-5608

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Language: English

Page range: 1 - 28

Submitted on: Sep 29, 2020

Published on: Dec 30, 2020

Published by: International Journal on Smart Sensing and Intelligent Systems

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

Comfort,

Intelligent building,

SVM,

Related subjects:

Introductions and overviews,

Engineering, other

© 2020 Henni Sid Ahmed, Jean Caelen, published by International Journal on Smart Sensing and Intelligent Systems
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 13 (2020): Issue 1 (January 2020)

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

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