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Performance metrics values

Performance metrics values 6L6n8
We can call the values in Figure 21-A the positive values, since the higher these values, the better the performance of the pre-trained network and its higher ability to classify correctly, and vice versa. Whereas, the values in Figure 21-B can be called the negative values, where the more these values decrease and approach zero, the better the performance of the pre-trained network, and its classifier tends to be perfect, and therefore the wrong classification is very low or almost non-existent, and vice versa. From Figure 21-A and Figure 21-B that show the values of the performance metrics for five the pre-trained networks(MobileNetV2, AlexNet, GoogleNet, ResNet 18, and ResNet 50) used in this study. All results for all pre-trained networks that used in the current study scored high results but we can see that the best values of the performance metrics were recorded by ResNet 50+ TL+KFCV, where average positive values which included in Figure 21-A (98. 59% Accuracy, 97. 9% Sensitivity, 98. 9% Specificity, 97. 9% Precision, 97. 9 % F1-Score, and 98. 9% Negative predicted value). Thus, the ResNet50 recorded the highest positive values listed in Figure 21-A among all the pre-trained networks used in this research. Also, we can see from average negative values which included in Figure 21-B( 1. 4%Error, 1. 05% False positive rate, 2. 1% False negative rate, 2. 1%False discovery rate). Thus, the ResNet50 recorded the lowest negative values listed in Figure 21-B among all the pre-trained networks used in this research.
We can call the values in
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Figure
21-A the
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positive
values, since the higher these values, the better the
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performance
of the pre-trained
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network
and its higher ability to classify
correctly
, and vice versa.

Whereas, the values in
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Figure
21-B can
be called
the
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negative
values, where the more these values decrease and approach zero, the better the
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performance
of the pre-trained
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network
, and its classifier tends to be perfect, and
therefore
the
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wrong
classification is
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very
low or almost non-existent, and vice versa.

From
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Figure
21-A and
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Figure
21-B that
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show
the values of the
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performance
metrics for five the pre-trained networks(MobileNetV2,
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AlexNet
,
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GoogleNet
,
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ResNet
18, and
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ResNet
50)
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used
in this study. All results for all pre-trained
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networks
that
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used
in the
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current
study scored high results
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but
we can
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see
that the best values of the
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performance
metrics
were recorded
by
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ResNet
50+ TL+
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KFCV
, where average
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positive
values which included in
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Figure
21-A (98. 59% Accuracy, 97. 9% Sensitivity, 98. 9% Specificity, 97.
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9%
Precision, 97. 9 % F1-Score, and 98. 9%
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Negative
predicted value).

Thus
, the ResNet50 recorded the highest
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positive
values listed in
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Figure
21-A among all the pre-trained
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networks
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used
in this research.

Also
, we can
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see
from average
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negative
values which included in
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Figure
21-B
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(
1. 4%Error, 1. 05% False
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positive
rate, 2. 1% False
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negative
rate, 2. 1%False discovery rate).

Thus
, the ResNet50 recorded the lowest
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negative
values listed in
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Figure
21-B among all the pre-trained
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networks
This phrase/word (depending on the context) can be potentially replaced with :
used
in this research.
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IELTS essay Performance metrics values

Essay
  American English
6 paragraphs
249 words
This writing has been penalized,
text can't be
less than 250 words in Task 2
and less than 150 words in Task 1
5.0
Overall Band Score
Coherence and Cohesion: 5.5
  • Structure your answers in logical paragraphs
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    One main idea per paragraph
  • Include an introduction and conclusion
  • Support main points with an explanation and then an example
  • Use cohesive linking words accurately and appropriately
  • Vary your linking phrases using synonyms
Lexical Resource: 5.0
  • Try to vary your vocabulary using accurate synonyms
  • Use less common question specific words that accurately convey meaning
  • Check your work for spelling and word formation mistakes
Grammatical Range: 6.5
  • Use a variety of complex and simple sentences
  • Check your writing for errors
Task Achievement: 5.0
  • Answer all parts of the question
  • ?
    Present relevant ideas
  • Fully explain these ideas
  • Support ideas with relevant, specific examples
Labels Descriptions
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    Currently is not available
  • Meet the criteria
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