"Lesotho Leaps Forward: A Chronicle of Transformative Developments"
Ahishek Intership PPT.pptx
1. ABHISHEK R
1HK20IS002
HKBKCE 1
NANO ROBOTICS EMBED TECHNOLOGIES
from
HKBK COLLEGE OF ENGINEERING
Department of Information Science & Engineering
2. NANO ROBOTICS EMBED TECHNOLOGIES
INTRODUCTION
Nano Robotics Embed Technologies in Mathikere, Bangalore Nano 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 Centre, Embedded System Training Institute, 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.
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3. NANO ROBOTICS EMBED TECHNOLOGIES
MISSION
1. To provide more value per dollar to our clients by providing timely and qualitative
services/solutions and attain utmost client satisfaction through skill building,
innovation and best practiced processes.
2. To offer total, cost-effective, next generation embedded hardware and software
solutions in the shortest possible development time enabling our clients to launch their
product ideas early.
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4. NANO ROBOTICS EMBED TECHNOLOGIES
VISION
1. To bring best of the human assets by providing environment for grooming, nurturing,
and growing talent to foster human growth and providing services and solutions to
the IT companies globally creating value for ourcustomers.
2. To lead in embedded hardware and software solutions and be known as an
electronic product development company of repute.
3. To build strategic partnerships globally with all stakeholders - clients, vendors, and
investors.
4. To stay abreast with technology and build our technical competence and domain
expertise.
5. To nurture a winning team that has a passion for excellence.
6. To be the delight of their customers by achieving perfection in their processes and
quality methods.
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5. 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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6. STRUCTURE OF INTERNSHIP PROGRAM
WEEK-01
Introduction
Introduction on Matplotlib,
Pandas,Numpy
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.
8. I
N
T
R
O
D
U
C
T
I
O
N
✔ A deep CNN architecture we are building in this project 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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9. 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 project 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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11. Effect of data augmentation techniques on an X-ray
image.
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(a) Original.
(a) Original. (b) Flipped. .
(c) Rotated 90°.
(d) Rotated
180°. (e) Rotated
360°.
12. Screen Shot of Results
Predicted Normal Result of a patient
Using Radiographs
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13. Screen Shot of Results
Predicted COVID Positive Result of a
patient Using Radiographs
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14. CONCLUSION
At Contoso, we believe in giving 110%. By using our next-generation data
architecture, we help organizations virtually manage agile workflows.
"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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16. WORKED PROJECTS
PROJECT-01
Using the concept of
Support Vector
Machine(SVM)
PROJECT-02
(Final Project)
Using the Concept of
convolutional neural
networks (CNNs)
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