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Accuracy in the three pre-trained networks

Accuracy in the three pre-trained networks gDDa7
3. 1. Accuracy in the three pre-trained networks Table 3 shows the training iterations of using the AlexNet +TL architecture with GPU in Matlab. It is clear that as the iteration number increases, the accuracy of the image classification goes up while the loss decreases. Similar outcomes can be witnessed from figures. 8, 9 and 10. The experimental studies have shown that the total detection accuracy of this system reaches 92. 5%, 87. 4%, 85. 0% and 85. 1% when we use AlexNet+TL, ResNet 18+TL, GoogleNet+TL and AlexNet+SVM respectively. In our proposed system, ten different groups were classified (nine types of diseases and one healthy group) with an accuracy rate of 92. 5% by AlexNet+TL. Figure 11 shows the confusion matrix of using AlexNet+TL is the most successful technique to test on our dataset. Table 4 shows the number of correctly classified images, incorrectly classified images and accuracy in the testing stage. Looking at table 4, one can see that high classification accuracy rates are obtained with the AlexNet+TL architecture. There are different accuracy figures, changing between 85% to 100% with a noticeable average value of 92. 5%. These discrepancies may be due to the image contrast and similarities for some diseases in the dataset. Taking into consideration the nature of the image dataset, we obtain a high classification accuracy rate that can be useful for farmers and agricultural engineers. Also, the AlexNet may be applied to different images for different situations. In our study, CUDA code was used to speed up the processing in GPU by accelerating the processing of deep learning frameworks. In practice, the GPU takes 3: 46, 3: 07, 6: 55, 1. 35 mins in AlexNet+TL,
3. 1.
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Accuracy
in the three pre-trained networks

Table 3
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shows
the training iterations of using the
Possible spelling mistake found.
AlexNet
+TL architecture with GPU in Matlab. It is
This phrase/word (depending on the context) can be potentially replaced with :
clear
that as the iteration number increases, the
You use this word too often, consider replacing it with
accuracy
of the
You use this word too often, consider replacing it with
image
classification goes up while the loss decreases. Similar outcomes can
be witnessed
from figures. 8, 9 and 10. The experimental studies have shown that the total detection
You use this word too often, consider replacing it with
accuracy
of this system reaches 92. 5%, 87. 4%, 85. 0% and 85. 1% when we
This phrase/word (depending on the context) can be potentially replaced with :
use
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AlexNet
+TL,
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ResNet
18+TL,
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GoogleNet
+TL and
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AlexNet
+SVM
respectively
.

In our proposed system, ten
You use this word too often, consider replacing it with
different
groups
were classified
(nine types of diseases and one healthy group) with an
You use this word too often, consider replacing it with
accuracy
rate of 92. 5% by
Possible spelling mistake found.
AlexNet
+TL. Figure 11
This phrase/word (depending on the context) can be potentially replaced with :
shows
the confusion matrix of using
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AlexNet
+TL is the most successful technique to
This phrase/word (depending on the context) can be potentially replaced with :
test
on our dataset.

Table 4
This phrase/word (depending on the context) can be potentially replaced with :
shows
the number of
correctly
classified
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images
,
incorrectly
classified
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images
and
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accuracy
in the testing stage. Looking at table 4, one can
This phrase/word (depending on the context) can be potentially replaced with :
see
that high classification
You use this word too often, consider replacing it with
accuracy
rates
are obtained
with the
Possible spelling mistake found.
AlexNet
+TL architecture. There are
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different
You use this word too often, consider replacing it with
accuracy
figures, changing between 85% to 100% with a noticeable average value of 92. 5%. These discrepancies may be due to the
You use this word too often, consider replacing it with
image
contrast and similarities for
This phrase/word is too simple and can be replaced with:
some
diseases in the dataset. Taking into consideration the nature of the
You use this word too often, consider replacing it with
image
dataset, we obtain a high classification
You use this word too often, consider replacing it with
accuracy
rate that can be useful for farmers and agricultural engineers.
Also
, the
Possible spelling mistake found.
AlexNet
may
be applied
to
You use this word too often, consider replacing it with
different
You use this word too often, consider replacing it with
images
for
You use this word too often, consider replacing it with
different
situations. In our study,
Possible spelling mistake found.
CUDA
code was
This phrase/word (depending on the context) can be potentially replaced with :
used
to
This phrase/word (depending on the context) can be potentially replaced with :
speed up
the processing in GPU by accelerating the processing of deep learning frameworks. In practice, the GPU takes 3: 46, 3: 07, 6: 55, 1. 35
Possible spelling mistake found.
mins
in
Possible spelling mistake found.
AlexNet
+TL,
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IELTS academic Accuracy in the three pre-trained networks

Academic
  American English
3 paragraphs
280 words
6.0
Overall Band Score
Coherence and Cohesion: 6.5
  • Structure your answers in logical paragraphs
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    One main idea per paragraph
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    Include an introduction and conclusion
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    Support main points with an explanation and then an example
  • Use cohesive linking words accurately and appropriately
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    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
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    Present relevant ideas
  • Fully explain these ideas
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    Support ideas with relevant, specific examples
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