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Chapter four : Simulation Results

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Table 4: Proposed model training during the prediction of energy consumption (ANN)

Proposed Model Training

Input

Total number of samples (15961)

Result (output)

Expected output Predicted Positive Predicted Negative True Positive (T.P.) False Positive (F.P.)

15261 Positive 13864 1397

False Negative (F.N.) True Negative (TN)

700 Negative 107 593

Table 5: Proposed model validation during the prediction of intrusion (ANN)

Proposed Model Validation

Input

Total number of samples (6841)

Result (output)

Expected output Predicted Positive Predicted Negative True Positive (T.P.) False Positive (F.P.)

6538 Positive 5741 797

False Negative (F.N.) True Negative (TN)

303 Negative 79 224

SVM and ANN approaches are being used on the dataset of 22802 sets of records; moreover, the dataset is divided into training constitutes of 70% (15961 samples) and 30% (6841 samples) for the revealed purposes of training and validation. Diverse processes used for performance calculation with various metrics named accuracy, sensitivity, specificity, miss-rate, fall-out, Positive Likelihood Ratio (LR+), Likelihood Negative Ration (L.R.-), Precision and Negative Predictive Value whereas the True Positive Rate (TPR) is expressed as sensitivity, True Negative

58 Rate (TNR) as specificity, False Negative Rate (FNR) as miss-rate, False-Positive Rate (FPR) as fall-out and Positive Predictive Value (PPV) as precision. The formulas are given as:

𝑆𝑒𝑛𝑠𝑖𝑑𝑖𝑣𝑖𝑑𝑦 =

βˆ‘ π‘‡π‘Ÿπ‘’π‘’ π‘ƒπ‘œπ‘ π‘–π‘‘π‘–π‘£π‘’

βˆ‘ πΆπ‘œπ‘›π‘‘π‘–π‘‘π‘–π‘œπ‘› π‘ƒπ‘œπ‘ π‘–π‘‘π‘–π‘£π‘’

(26)

𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑑𝑦 =

βˆ‘ π‘‡π‘Ÿπ‘’π‘’ π‘π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’

βˆ‘ πΆπ‘œπ‘›π‘‘π‘–π‘‘π‘–π‘œπ‘› π‘π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’

(27)

π΄π‘π‘π‘’π‘Ÿπ‘Žπ‘π‘¦ =

βˆ‘ π‘‡π‘Ÿπ‘’π‘’ π‘ƒπ‘œπ‘ π‘–π‘‘π‘–π‘£π‘’+ βˆ‘ π‘‡π‘Ÿπ‘’π‘’ π‘ƒπ‘œπ‘ π‘–π‘‘π‘–π‘£π‘’

βˆ‘ π‘‡π‘œπ‘‘π‘Žπ‘™ π‘ƒπ‘œπ‘π‘’π‘™π‘Žπ‘‘π‘–π‘œπ‘›

(28) 𝑀𝑖𝑠𝑠 βˆ’ π‘…π‘Žπ‘‘π‘’ =

βˆ‘ πΉπ‘Žπ‘™π‘ π‘’ π‘π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’

βˆ‘ πΆπ‘œπ‘›π‘‘π‘–π‘‘π‘–π‘œπ‘› π‘ƒπ‘œπ‘ π‘–π‘‘π‘–π‘£π‘’

(29) πΉπ‘Žπ‘™π‘™π‘œπ‘’π‘‘ =

βˆ‘ πΉπ‘Žπ‘™π‘ π‘’ π‘ƒπ‘œπ‘ π‘–π‘‘π‘–π‘£π‘’

βˆ‘ πΆπ‘œπ‘›π‘‘π‘–π‘‘π‘–π‘œπ‘› π‘π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’

(30)

πΏπ‘–π‘˜π‘’π‘™π‘–β„Žπ‘œπ‘œπ‘‘ π‘ƒπ‘œπ‘ π‘–π‘‘π‘–π‘£π‘’ π‘…π‘Žπ‘‘π‘–π‘œ =

βˆ‘ π‘‡π‘Ÿπ‘’π‘’ π‘ƒπ‘œπ‘ π‘–π‘‘π‘–π‘£π‘’ π‘…π‘Žπ‘‘π‘–π‘œ

βˆ‘ πΉπ‘Žπ‘™π‘ π‘’ π‘ƒπ‘œπ‘ π‘–π‘‘π‘–π‘£π‘’ π‘…π‘Žπ‘‘π‘–π‘œ

(31)

πΏπ‘–π‘˜π‘’π‘™π‘–β„Žπ‘œπ‘œπ‘‘ π‘π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’ π‘…π‘Žπ‘‘π‘–π‘œ =

βˆ‘ π‘‡π‘Ÿπ‘’π‘’ π‘ƒπ‘œπ‘ π‘–π‘‘π‘–π‘£π‘’ π‘…π‘Žπ‘‘π‘–π‘œ

βˆ‘ πΉπ‘Žπ‘™π‘ π‘’ π‘ƒπ‘œπ‘ π‘–π‘‘π‘–π‘£π‘’ π‘…π‘Žπ‘‘π‘–π‘œ

(32)

π‘ƒπ‘œπ‘ π‘–π‘‘π‘–π‘£π‘’ π‘ƒπ‘Ÿπ‘’π‘‘π‘–π‘π‘‘π‘–π‘£π‘’ π‘‰π‘Žπ‘™π‘’π‘’ =

βˆ‘ π‘‡π‘Ÿπ‘’π‘’ π‘ƒπ‘œπ‘ π‘–π‘‘π‘–π‘£π‘’

βˆ‘ π‘ƒπ‘Ÿπ‘’π‘‘π‘–π‘π‘‘π‘’π‘‘ πΆπ‘œπ‘›π‘‘π‘–π‘‘π‘–π‘œπ‘› π‘ƒπ‘œπ‘ π‘–π‘‘π‘–π‘£π‘’

(33) π‘π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’ π‘ƒπ‘Ÿπ‘’π‘‘π‘–π‘π‘‘π‘–π‘£π‘’ π‘‰π‘Žπ‘™π‘’π‘’ =

βˆ‘ π‘‡π‘Ÿπ‘’π‘’ π‘π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’

βˆ‘ π‘ƒπ‘Ÿπ‘’π‘‘π‘–π‘π‘‘π‘’π‘‘ πΆπ‘œπ‘›π‘‘π‘–π‘‘π‘–π‘œπ‘› π‘π‘’π‘”π‘Žπ‘‘π‘–π‘£π‘’

(34)

It is shown in table 2 that the proposed system prediction of energy consumption in the training of SVM. During training, a total of 15861 samples are used, which are divided into 15261,600 positive and negative samples, respectively. 13700 true positives are successfully predicted, and no energy consumption is identified, but 1561 records are mistakenly predicted as negatives, indicating energy consumption. Similarly, 600 samples are obtained, with negative showing energy consumption and positive showing no energy consumption. With 580 samples correctly identified as negative showing energy consumption and 20 samples inaccurately predicted as positive, indicating no energy consumption despite the existence of energy consumption.

It is shown in table 3 that the proposed system prediction of energy consumption in the training of SVM. 6763 samples are used during training, divided into 6538,225 positive and negative samples, respectively. 5618 true positives are successfully predicted, and no energy consumption is identified, but 920 records are mistakenly predicted as negatives, indicating energy

59 consumption. Similarly, 225 samples are obtained, with negative showing energy consumption and positive showing no energy consumption. With 217 samples correctly identified as negative showing, energy consumption and 8 samples inaccurately predicted as positive, indicating no energy consumption despite the existence of energy consumption.

It is shown in table 4 that the proposed system prediction of energy consumption in the training of ANN. During training, a total of 15961 samples are used, which are divided into 15261,700 positive and negative samples, respectively. 13864 true positives are successfully predicted, and no energy consumption is identified, but 1397 records are mistakenly predicted as negatives, indicating energy consumption. Similarly, 700 samples are obtained, with negative showing energy consumption and positive showing no energy consumption. 593 samples identified adequately as negative, indicating energy consumption, and 107 samples were inaccurately predicted as positive, indicating no energy consumption despite the existence of energy consumption.

It is shown in table 5 that the proposed model of energy consumption in the training of ANN.

During training, a total of 6841 samples are used, which are divided into 6538,303 positive and negative samples, respectively. 5741 true positives are successfully predicted, and no energy consumption is identified, but 797 records are mistakenly predicted as negatives, indicating energy consumption. Similarly, 303 samples are obtained, with negative showing energy consumption and positive showing no energy consumption. With 224 samples correctly identified as negative showing energy consumption and 79 samples inaccurately predicted as positive indicating no energy consumption despite the existence of energy consumption.

Table 1: Performance evaluation of proposed energy consumption model in training and validation using different statistical measures (SVM)

SVM Accuracy Sensitivity TPR

Specificity TNR

Miss- Rate (%)

FNR

Fall-out FPR

LR+ LR- PPV (Precision)

NPV

Training 0.894 0.897 0.828 0.106 0.171 5.245 0.128 0.991 0.270

Validation 0.862 0.859 0.964 0.138 0.035 124.6

28

0.143 0.998 0.190

60 It is shown in Table 5 (SVM) that the proposed model learning technique performance in terms of accuracy sensitivity, specificity, miss rate, and precision during the training and validation phase. It shows that the proposed model throughout training gives 0.894, 0.897, 0.828, 0.106, and 0.991. And during validation, the proposed model provides 0.862, 0.859, 0.964, 0.138, and 0.998 in terms of accuracy sensitivity, specificity, miss rate, and precision, respectively.

In addition, some more statistical measures of the proposed system during training predict the values 0.171, 5.245, 0.128, and 0.270, whereas, in validation, 0.035, 124.628, 0.143, and 0.190, in terms of fall out, positive likelihood ratio, likelihood negative ratio, and negative predictive value.

Table 2: Performance evaluation of proposed energy consumption model in training and validation using different statistical measures (ANN)

ANN Accuracy Understan ding TPR

Specificity TNR

Miss-Rate (%) FNR

Fall-out FPR

LR+ LR- PPV

(Precision)

NPV

Training 0.905 0.908 0.847 0.054 0.152 5.973 0.0637 0.992 0.298

Validation 0.873 0.878 0.761 0.127 0.238 3.689 0.166 0.988 0.219

Table 6 (D.T.) shows the proposed system learning technique performance in terms of accuracy sensitivity, specificity, miss rate, and accuracy throughout the training and validation phase. It clearly shows that the proposed model during training gives 0.905, 0.908, 0.847, 0.054, and 0.992 accuracy sensitivity, specificity, miss rate, and precision. And throughout validation, the proposed model provides 0.873, 0.878, 0.761, 0.127, and 0.988 in terms of accuracy sensitivity, specificity, miss rate, and precision, respectively. In addition, some more statistical measure of the proposed system is added to predict the values, such as fall out likelihood positive ratio, likelihood negative ratio, and negative predictive value.

Table 3: Fusion results of the proposed Smart Energy Consumption system Empowered with fussed Machine Learning techniques (SVM and ANN)

S.

NO.

SVM ANN The proposed

(SID-FLFEF- ML)

The human specialist decision of SID-

FLFEF-ML

Chance of correctness

Chance of errors

1 89.4 (Yes) 91.4 (Yes) 84.7 (Yes) Yes 1 0

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2 28.1 (No) 41.1 (No) 50 (No) Yes 1 0

3 27.1 (No) 24.1 (No) 50 (No) No 1 0

4 27.1 (No) 24.1 (No) 50 (No) No 1 0

5 27.1 (No) 24.1 (No) 50 (No) No 1 0

6 27.1 (No) 24.1 (No) 50 (No) No 1 0

7 28.1 (No) 71.1 (Yes) 84.7 (Yes) Yes 1 0

8 28.1 (No) 71.1 (Yes) 84.7 (Yes) Yes 1 0

9 28.1 (No) 71.1 (Yes) 84.7 (Yes) Yes 1 0

10 28.1 (No) 71.1 (Yes) 84.7 (Yes) Yes 1 0

11 91.3 (Yes) 71.1 (Yes) 84.7 (Yes) Yes 1 0

12 91.3 (Yes) 71.1 (Yes) 84.7 (Yes) Yes 1 0

13 91.3 (Yes) 71.1 (Yes) 50 (No) Yes 0 1

Table 4: Comparison of performance of the proposed system using SVM and ANN algorithms

SVM Accuracy 0.862

Miss rate 0.138

ANN Accuracy 0.873

Miss rate 0.127 Fusion based Machine

learning Approach

Accuracy 0.923 Miss rate 0.077

It is shown in table 8 that there are 13 tests taken in which only one is opposite to the proposed model and human-based decision, which shows 0.923 accuracies of the proposed system. Also, it is shown in table 8 that the comparison of the performance of the proposed system clearly shows that the SVM accuracy and miss rate are 0.862 and 0.138, and in ANN, it is 0.873 and 0.127,

62 respectively. It is demonstrated that the performance of the intended fusion-based technique is 0.923 accuracy and 0.077 miss rate.

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