2. NANO ROBOTICS EMBED TECHNOLOGIES
INTRODUCTION
Nano Robotics Embed Technologies in Mathikere, BangaloreNano Robotics Embed Technologies in Bangalore is
one of the leading businesses in the Engineering Project Consultants. Also known for Project Consultants,
Programming Training Institutes, Engineering Project Consultants, Project Report Consultants, Project
Management Consultants, Artificial Intelligence Training Centres, Embedded System Training Institutes,
Management Consultants and much more. NRET was established in the year 2014 and it is a top player in the
category Engineering Project Consultants in the Bangalore. This well-known establishment acts as a one-stop
destination servicing customers both local and from other parts of Bangalore. Over the course of its journey, this
business has established a firm foothold in it's industry. The belief that customer satisfaction is as important as their
products and services, have helped this establishment garner a vast base of customers, which continues to grow by
the day. This business employs individuals that are dedicated towards their respective roles and put in a lot of effort
to achieve the common vision and larger goals of the company. In the near future, this business aims to expand its
line of products and services and cater to a larger client base. In Bangalore, this establishment occupies a
prominent location in Mathikere.
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3. ARTIFICIAL INTELLIGENCE
✔ Artificial Intelligence (AI) is a field of computer science focused on
creating intelligent machines capable of human-like tasks.
✔ AI applications span various industries, from healthcare and finance to
autonomous vehicles, revolutionizing how we live and work.
✔ The evolution of AI has led to significant advancements, particularly in
machine learning and deep learning techniques.
✔ Ethical considerations, such as algorithmic bias and privacy concerns,
accompany AI's rapid development and adoption.
✔ Responsible AI development and regulation are essential to harness the
full potential of AI while mitigating its potential risks.
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4. STRUCTURE OF INTERNSHIP PROGRAM
WEEK-01
Introduction
Introduction on Matplolib
Types of machine learning
WEEK-02
Linear regression
KNN Algorithm
Project on Salary-Data
Introduction on Neural
Network
WEEK-03
Analyzing Neural Network
using Python
Working on Project
Convolution Neural
Network(CNN)
WEEK-04
K-Means Cluster algorithm
Support Vector Machine
(SVM) Algorithm
Project on K-Means &
CNN
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Basically there where four-week internship program, we acquired a comprehensive understanding of artificial intelligence. In the final
week, we were tasked with applying our knowledge to a project, allowing us to demonstrate our skills and expertise effectively.
Structure of complete four-weeks is given below
6. I
N
T
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O
D
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✔ A deep CNN architecture we are proposing in this paper for the
diagnosis of COVID-19 based on the chest X-ray image
classification.
✔ Artificial intelligence (AI) techniques in general and convolutional
neural networks (CNNs) in particular have attained successful
results in medical image analysis and classification
✔ Due to the nonavailability of sufficient-size and good-quality chest
X-ray image dataset, an effective and accurate CNN classification
was a challenge.
✔ To deal with these complexities such as the availability of a very-
small-sized and imbalanced dataset with image-quality issues, the
dataset has been preprocessed in different phases using different
techniques to achieve an effective training dataset for the proposed
CNN model to attain its best performance.
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7. Abstract
✔ Artificial intelligence (AI) techniques in general and convolutional neural networks (CNNs) in particular
have attained successful results in medical image analysis and classification. A deep CNN architecture
has been proposed in this paper for the diagnosis of COVID-19 based on the chest X-ray image
classification. Due to the nonavailability of sufficient-size and good-quality chest X-ray image dataset, an
effective and accurate CNN classification was a challenge. To deal with these complexities such as the
availability of a very-small-sized and imbalanced dataset with image-quality issues, the dataset has been
preprocessed in different phases using different techniques to achieve an effective training dataset for the
proposed CNN model to attain its best performance.
✔ The preprocessing stages of the datasets performed in this study include dataset balancing, medical
experts’ image analysis, and data augmentation. The experimental results have shown the overall
accuracy as high as 99.5% which demonstrates the good capability of the proposed CNN model in the
current application domain. The CNN model has been tested in two scenarios. In the first scenario, the
model has been tested using the 100 X-ray images of the original processed dataset which achieved an
accuracy of 100%. In the second scenario, the model has been tested using an independent dataset of
COVID-19 X-ray images. The performance in this test scenario was as high as 99.5%.
✔ To further prove that the proposed model outperforms other models, a comparative analysis has been
done with some of the machine learning algorithms. The proposed model has outperformed all the models
generally and specifically when the model testing was done using an independent testing set.
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9. Screen Shot of Results
Predicted Normal Result of a patient
Using Radiographs
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10. Screen Shot of Results
Predicted COVID Positive Result of a
patient Using Radiographs
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11. CONCLUSION
At Contoso, we believe in giving 110%. By using our next-generation data
architecture, we help organizations virtually manage agile workflows. We
thrive because of our market knowledge and great team behind our product.
As our CEO says, "Efficiencies will come from proactively transforming how
we This study has been conducted to demonstrate the effective and accurate
diagnosis of COVID-19 using CNN which was trained on chest X-ray image
datasets. The model training was performed incrementally with different
datasets to attain the maximum accuracy and performance. The primary
dataset was very limited in size and also imbalanced in terms of class
distribution. These two issues with the primary dataset affected the
performance of the models very badly. To overcome these issues, the dataset
was preprocessed using different techniques, including dataset balancing
technique, manual analysis of X-ray images by concerned medical experts,
and data augmentation techniques. To balance the dataset for model training
and also to test its performance parameters, an ample number of chest X-
rays were collected from different available sources. After training and testing
the CNN model on the fully processed dataset, the performance results have
been reported.do business."
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