Course Title: CS591-Advance Artificial Intelligence StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara SEMOID#S02023694
StudentEmail: [email protected] Date:04/20/2021
Violations of academic honesty represent a serious breach of discipline and may be considered grounds for disciplinary action, including dismissal
from the University. The University requires that all assignments submitted to faculty members by students be the work of the individual student
submitting the work. An exception would be group projects assigned by the instructor. (Source: SEMO website)
Advanced Artificial Intelligence Assignment
Graduate project level 2
Abstract
Artificial Intelligence (AI) is a crucial technical technology that is commonly used in today's
society. Deep Learning, in particular, has a variety of uses due to its ability to learn robust
representations from images. A Convolutional Neural Network (CNN) is a Deep Learning
algorithm which commands the input image, assigns significance to numerous aspects/objects in
the image, and can distinguish between them. For image classification, CNN is the most popular
Deep Learning architecture. To get better results, we used various automated processing tasks for
fruit and vegetable images. In comparison to other classification deep learning algorithms, the
amount of pre-processing needed by a CNN model is much lower. Furthermore, the learning
capabilities of Deep Learning architectures can be used to improve sound classification in order
to solve efficiency problems. CNN is used in this project, and layers are created to classify the
sound waves into their various categories.
Introduction
We humans enjoy analyzing items, and everything you can think of can be classified into a
classification or class. It is an everyday issue in business; analysis of parts, installations,
gatherings, and products are necessary for the daily routine. This is the reason why people have
devised procedures such as Machine Learning (ML), Neural Networks (NN), and Deep Learning
(DL), among other calculations, to automate the arrangement period. Deep learning will be one
of them that we will explore. Deep learning is an artificial intelligence (AI) function that
simulates how the human brain processes data and creates patterns to make decisions. The
classification of photographs of fruits and vegetables with the naked eye is very difficult. As a
result, we're using pyTorch to process image datasets with Deep Learning. We're developing a
CNN model for image detection and categorization using these datasets. A custom CNN is
introduced and then compared to a ResNet CNN for the purposes of this study. The oth ...
One-shot learning is an object categorization problem in computer vision. Whereas most machine learning based object categorization algorithms require training on hundreds or thousands of images and very large datasets, one-shot learning aims to learn information about object categories from one, or only a few, training images
Image Classification with Deep Learning Techniques and Challenges.pptxMicrosoft azure
Deep Learning Institute in Noida, particularly Convolutional Neural Networks (CNNs), has transformed picture categorization by producing cutting-edge results across several domains. In this blog, we will look at the approaches used in deep learning-based picture categorization and the issues that researchers and developers confront in this domain.
This is the Bangla Handwritten Digit Recognition Report. you can see this report for your helping hand.
**Bengali is the world's fifth most spoken language, with 265 million native and non-native speakers accounting for 4% of the global population.
**Despite the large number of Bengali speakers, very little research has been conducted on Bangali handwritten digit recognition.
**The application of the BHwDR system is wide from postal code digit recognition to license plate recognition, digit recognition in cheques in the banking system to exam paper registration number recognition.
Image Captioning Generator using Deep Machine Learningijtsrd
Technologys scope has evolved into one of the most powerful tools for human development in a variety of fields.AI and machine learning have become one of the most powerful tools for completing tasks quickly and accurately without the need for human intervention. This project demonstrates how deep machine learning can be used to create a caption or a sentence for a given picture. This can be used for visually impaired persons, as well as automobiles for self identification, and for various applications to verify quickly and easily. The Convolutional Neural Network CNN is used to describe the alphabet, and the Long Short Term Memory LSTM is used to organize the right meaningful sentences in this model. The flicker 8k and flicker 30k datasets were used to train this. Sreejith S P | Vijayakumar A "Image Captioning Generator using Deep Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42344.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42344/image-captioning-generator-using-deep-machine-learning/sreejith-s-p
One-shot learning is an object categorization problem in computer vision. Whereas most machine learning based object categorization algorithms require training on hundreds or thousands of images and very large datasets, one-shot learning aims to learn information about object categories from one, or only a few, training images
Image Classification with Deep Learning Techniques and Challenges.pptxMicrosoft azure
Deep Learning Institute in Noida, particularly Convolutional Neural Networks (CNNs), has transformed picture categorization by producing cutting-edge results across several domains. In this blog, we will look at the approaches used in deep learning-based picture categorization and the issues that researchers and developers confront in this domain.
This is the Bangla Handwritten Digit Recognition Report. you can see this report for your helping hand.
**Bengali is the world's fifth most spoken language, with 265 million native and non-native speakers accounting for 4% of the global population.
**Despite the large number of Bengali speakers, very little research has been conducted on Bangali handwritten digit recognition.
**The application of the BHwDR system is wide from postal code digit recognition to license plate recognition, digit recognition in cheques in the banking system to exam paper registration number recognition.
Image Captioning Generator using Deep Machine Learningijtsrd
Technologys scope has evolved into one of the most powerful tools for human development in a variety of fields.AI and machine learning have become one of the most powerful tools for completing tasks quickly and accurately without the need for human intervention. This project demonstrates how deep machine learning can be used to create a caption or a sentence for a given picture. This can be used for visually impaired persons, as well as automobiles for self identification, and for various applications to verify quickly and easily. The Convolutional Neural Network CNN is used to describe the alphabet, and the Long Short Term Memory LSTM is used to organize the right meaningful sentences in this model. The flicker 8k and flicker 30k datasets were used to train this. Sreejith S P | Vijayakumar A "Image Captioning Generator using Deep Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42344.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42344/image-captioning-generator-using-deep-machine-learning/sreejith-s-p
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification.
This work highlights transfer learning’s effectiveness in image classification using CNNs and VGG 16 that
provides insights into the selection of pre-trained models and hyper parameters for optimal performance.
We have proposed a comprehensive approach for image segmentation and classification, incorporating preprocessing techniques, the K-means algorithm for segmentation, and employing deep learning models such
as CNN and VGG 16 for classification.
An Extensive Review on Generative Adversarial Networks GAN’sijtsrd
This paper is to provide a high level understanding of Generative Adversarial Networks. This paper will be covering the working of GAN’s by explaining the background idea of the framework, types of GAN’s in the industry, it’s advantages and disadvantages, history of how GAN’s are developed and enhanced along the timeline and some applications where GAN’s outperforms themselves. Atharva Chitnavis | Yogeshchandra Puranik "An Extensive Review on Generative Adversarial Networks (GAN’s)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42357.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42357/an-extensive-review-on-generative-adversarial-networks-gan’s/atharva-chitnavis
الموعد الإثنين 03 يناير 2022
143
مبادرة
#تواصل_تطوير
المحاضرة ال 143 من المبادرة
المهندس / محمد الرافعي طرباي
نقيب المبرمجين بالدقهلية
بعنوان
"IT INDUSTRY"
How To Getting Into IT With Zero Experience
وذلك يوم الإثنين 03 يناير2022
السابعة مساء توقيت القاهرة
الثامنة مساء توقيت مكة المكرمة
و الحضور من تطبيق زووم
https://us02web.zoom.us/meeting/register/tZUpf-GsrD4jH9N9AxO39J013c1D4bqJNTcu
علما ان هناك بث مباشر للمحاضرة على القنوات الخاصة بجمعية المهندسين المصريين
ونأمل أن نوفق في تقديم ما ينفع المهندس ومهمة الهندسة في عالمنا العربي
والله الموفق
للتواصل مع إدارة المبادرة عبر قناة التليجرام
https://t.me/EEAKSA
ومتابعة المبادرة والبث المباشر عبر نوافذنا المختلفة
رابط اللينكدان والمكتبة الالكترونية
https://www.linkedin.com/company/eeaksa-egyptian-engineers-association/
رابط قناة التويتر
https://twitter.com/eeaksa
رابط قناة الفيسبوك
https://www.facebook.com/EEAKSA
رابط قناة اليوتيوب
https://www.youtube.com/user/EEAchannal
رابط التسجيل العام للمحاضرات
https://forms.gle/vVmw7L187tiATRPw9
ملحوظة : توجد شهادات حضور مجانية لمن يسجل فى رابط التقيم اخر المحاضرة
Accuracy study of image classification for reverse vending machine waste segr...IJECEIAES
This study aims to create a sorting system with high accuracy that can classify various beverage containers based on types and separate them accordingly. This reverse vending machine (RVM) provides an image classification method and allows for recycling three types of beverage containers: drink carton boxes, polyethylene terephthalate (PET) bottles, and aluminium cans. The image classification method used in this project is transfer learning with convolutional neural networks (CNN). AlexNet, GoogLeNet, DenseNet201, InceptionResNetV2, InceptionV3, MobileNetV2, XceptionNet, ShuffleNet, ResNet 18, ResNet 50, and ResNet 101 are the neural networks that used in this project. This project will compare the F1- score and computational time among the eleven networks. The F1-score and computational time of image classification differs for each neural network. In this project, the AlexNet network gave the best F1-score, 97.50% with the shortest computational time, 2229.235 s among the eleven neural networks.
CNN FEATURES ARE ALSO GREAT AT UNSUPERVISED CLASSIFICATION cscpconf
This paper aims at providing insight on the transferability of deep CNN features to
unsupervised problems. We study the impact of different pretrained CNN feature extractors on
the problem of image set clustering for object classification as well as fine-grained
classification. We propose a rather straightforward pipeline combining deep-feature extraction
using a CNN pretrained on ImageNet and a classic clustering algorithm to classify sets of
images. This approach is compared to state-of-the-art algorithms in image-clustering and
provides better results. These results strengthen the belief that supervised training of deep CNN
on large datasets, with a large variability of classes, extracts better features than most carefully
designed engineering approaches, even for unsupervised tasks. We also validate our approach
on a robotic application, consisting in sorting and storing objects smartly based on clustering
Detection of medical instruments project- PART 1Sairam Adithya
this presentation is about a project done by me and my colleague related to computer vision. This project is used to classify the uploaded images of biomedical instruments into prominent ones like ECG, EEG, x-ray machine, CT, MRI, and so on. A website has been developed on which the user can upload any image he is unknown of and the model will tell what instrument it is along with a paragraph explaining the instrument in a crisp manner
Business and Government Relations Please respond to the following.docxCruzIbarra161
"Business and Government Relations" Please respond to the following:
Discuss the main reasons why a business should or should not be involved in political discussions or take a political stand. Use terms found in Chapter 9 to demonstrate your understanding of the material. You can submit your initial discussion post and responses in either written or video format (2-3 minutes or less).
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Business Continuity Planning Explain how components of the busine.docxCruzIbarra161
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Correct, complete, clear, and concise.
.
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Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification.
This work highlights transfer learning’s effectiveness in image classification using CNNs and VGG 16 that
provides insights into the selection of pre-trained models and hyper parameters for optimal performance.
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as CNN and VGG 16 for classification.
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This paper is to provide a high level understanding of Generative Adversarial Networks. This paper will be covering the working of GAN’s by explaining the background idea of the framework, types of GAN’s in the industry, it’s advantages and disadvantages, history of how GAN’s are developed and enhanced along the timeline and some applications where GAN’s outperforms themselves. Atharva Chitnavis | Yogeshchandra Puranik "An Extensive Review on Generative Adversarial Networks (GAN’s)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42357.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42357/an-extensive-review-on-generative-adversarial-networks-gan’s/atharva-chitnavis
الموعد الإثنين 03 يناير 2022
143
مبادرة
#تواصل_تطوير
المحاضرة ال 143 من المبادرة
المهندس / محمد الرافعي طرباي
نقيب المبرمجين بالدقهلية
بعنوان
"IT INDUSTRY"
How To Getting Into IT With Zero Experience
وذلك يوم الإثنين 03 يناير2022
السابعة مساء توقيت القاهرة
الثامنة مساء توقيت مكة المكرمة
و الحضور من تطبيق زووم
https://us02web.zoom.us/meeting/register/tZUpf-GsrD4jH9N9AxO39J013c1D4bqJNTcu
علما ان هناك بث مباشر للمحاضرة على القنوات الخاصة بجمعية المهندسين المصريين
ونأمل أن نوفق في تقديم ما ينفع المهندس ومهمة الهندسة في عالمنا العربي
والله الموفق
للتواصل مع إدارة المبادرة عبر قناة التليجرام
https://t.me/EEAKSA
ومتابعة المبادرة والبث المباشر عبر نوافذنا المختلفة
رابط اللينكدان والمكتبة الالكترونية
https://www.linkedin.com/company/eeaksa-egyptian-engineers-association/
رابط قناة التويتر
https://twitter.com/eeaksa
رابط قناة الفيسبوك
https://www.facebook.com/EEAKSA
رابط قناة اليوتيوب
https://www.youtube.com/user/EEAchannal
رابط التسجيل العام للمحاضرات
https://forms.gle/vVmw7L187tiATRPw9
ملحوظة : توجد شهادات حضور مجانية لمن يسجل فى رابط التقيم اخر المحاضرة
Accuracy study of image classification for reverse vending machine waste segr...IJECEIAES
This study aims to create a sorting system with high accuracy that can classify various beverage containers based on types and separate them accordingly. This reverse vending machine (RVM) provides an image classification method and allows for recycling three types of beverage containers: drink carton boxes, polyethylene terephthalate (PET) bottles, and aluminium cans. The image classification method used in this project is transfer learning with convolutional neural networks (CNN). AlexNet, GoogLeNet, DenseNet201, InceptionResNetV2, InceptionV3, MobileNetV2, XceptionNet, ShuffleNet, ResNet 18, ResNet 50, and ResNet 101 are the neural networks that used in this project. This project will compare the F1- score and computational time among the eleven networks. The F1-score and computational time of image classification differs for each neural network. In this project, the AlexNet network gave the best F1-score, 97.50% with the shortest computational time, 2229.235 s among the eleven neural networks.
CNN FEATURES ARE ALSO GREAT AT UNSUPERVISED CLASSIFICATION cscpconf
This paper aims at providing insight on the transferability of deep CNN features to
unsupervised problems. We study the impact of different pretrained CNN feature extractors on
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designed engineering approaches, even for unsupervised tasks. We also validate our approach
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Detection of medical instruments project- PART 1Sairam Adithya
this presentation is about a project done by me and my colleague related to computer vision. This project is used to classify the uploaded images of biomedical instruments into prominent ones like ECG, EEG, x-ray machine, CT, MRI, and so on. A website has been developed on which the user can upload any image he is unknown of and the model will tell what instrument it is along with a paragraph explaining the instrument in a crisp manner
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The proposal should include:
•
The public health initiative’s purpose, background, goals, and objectives
•
A description of the funding sources you selected and explanation of why you selected it over others
•
Eligibility and selection criteria for the funding source
•
An explanation of the funds needed and how the funds may be used
•
The adjusted total 5-year budget you completed in week 9 (include all instructor recommendations)
(8 sources/references)
.
BUDDHISMWEEK 3Cosmogony - Origin of the UniverseNature of .docxCruzIbarra161
BUDDHISM
WEEK 3
Cosmogony - Origin of the Universe
Nature of God/Creator
View of Human Nature
View of Good & Evil
View of Salvation
View of After Life
Practices and Rituals
Celebrations & Festivals
Week 3 - Sources
.
Build a binary search tree that holds first names.Create a menu .docxCruzIbarra161
Build a binary search tree that holds first names.
Create a menu with the following options.
Add a name to the list (will add a new node)
Delete a name from the list (will delete a node)
NEXT PAGE
à
Search for a name (will return if the name is in the tree or not)
Output the number of leaves in your tree
Output the tree (Complete an inorder traversal.)
.
Briefly describe the development of the string quartet. How would yo.docxCruzIbarra161
Briefly describe the development of the string quartet. How would you relate this chamber ensemble to modern performing groups such as the jazz quartet? Or to a rock ensemble? What are some of the similarities and differences? Refer to the listening examples in the Special Focus to support your conclusions.
Listening examples:
String Quartet in E-Flat, No. 2
("Joke") by Haydn
String Quartet in C Minor
by Beethoven
String Quartet No. 2, Op. 17
by Bartók
.
Briefly describe a time when you were misled by everyday observation.docxCruzIbarra161
Briefly describe a time when you were misled by everyday observations (that is when you reached a conclusion on the basis of an everyday observation that you later decided was an incorrect conclusion). What type of error in casual inquiry (sources of secondhand knowledge) were you guilty of? Examples include over-generalization, stereotyping, illogical reasoning, etc
.
Broadening Your Perspective 8-1The financial statements of Toots.docxCruzIbarra161
Broadening Your Perspective 8-1
The financial statements of Tootsie Roll are presented below.
TOOTSIE ROLL INDUSTRIES, INC. AND SUBSIDIARIES
CONSOLIDATED STATEMENTS OF
Earnings, Comprehensive Earnings and Retained Earnings (in thousands except per share data)
For the year ended December 31,
2011
2010
2009
Net product sales
$528,369
$517,149
$495,592
Rental and royalty revenue
4,136
4,299
3,739
Total revenue
532,505
521,448
499,331
Product cost of goods sold
365,225
349,334
319,775
Rental and royalty cost
1,038
1,088
852
Total costs
366,263
350,422
320,627
Product gross margin
163,144
167,815
175,817
Rental and royalty gross margin
3,098
3,211
2,887
Total gross margin
166,242
171,026
178,704
Selling, marketing and administrative expenses
108,276
106,316
103,755
Impairment charges
—
—
14,000
Earnings from operations
57,966
64,710
60,949
Other income (expense), net
2,946
8,358
2,100
Earnings before income taxes
60,912
73,068
63,049
Provision for income taxes
16,974
20,005
9,892
Net earnings
$43,938
$53,063
$53,157
Net earnings
$43,938
$53,063
$53,157
Other comprehensive earnings (loss)
(8,740
)
1,183
2,845
Comprehensive earnings
$35,198
$54,246
$56,002
Retained earnings at beginning of year.
$135,866
$147,687
$144,949
Net earnings
43,938
53,063
53,157
Cash dividends
(18,360
)
(18,078
)
(17,790
)
Stock dividends
(47,175
)
(46,806
)
(32,629
)
Retained earnings at end of year
$114,269
$135,866
$147,687
Earnings per share
$0.76
$0.90
$0.89
Average Common and Class B Common shares outstanding
57,892
58,685
59,425
(The accompanying notes are an integral part of these statements.)
CONSOLIDATED STATEMENTS OF
Financial Position
TOOTSIE ROLL INDUSTRIES, INC. AND SUBSIDIARIES (in thousands except per share data)
Assets
December 31,
2011
2010
CURRENT ASSETS:
Cash and cash equivalents
$78,612
$115,976
Investments
10,895
7,996
Accounts receivable trade, less allowances of $1,731 and $1,531
41,895
37,394
Other receivables
3,391
9,961
Inventories:
Finished goods and work-in-process
42,676
35,416
Raw materials and supplies
29,084
21,236
Prepaid expenses
5,070
6,499
Deferred income taxes
578
689
Total current assets
212,201
235,167
PROPERTY, PLANT AND EQUIPMENT, at cost:
Land
21,939
21,696
Buildings
107,567
102,934
Machinery and equipment
322,993
307,178
Construction in progress
2,598
9,243
455,097
440,974
Less—Accumulated depreciation
242,935
225,482
Net property, plant and equipment
212,162
215,492
OTHER ASSETS:
Goodwill
73,237
73,237
Trademarks
175,024
175,024
Investments
96,161
64,461
Split dollar officer life insurance
74,209
.
Briefly discuss the differences in the old Minimum Foundation Prog.docxCruzIbarra161
Briefly discuss the differences in the old Minimum Foundation Program ( 1947 ) and the FEFP ( 1973 ).
What part of the basic FEFP formula ( State Aid = WFTE x BSA - (.96 AV } provides A. equity for students and B. equalization of funding for districts?
Review how student transportation dollars are calculated. What are the two major components?
What is the function of Workforce Development funds?
What are Categorical Program funds? How do they differ from general FEFP funding?
What are the four constructs on which the FEFP is based? ( Page 1--2
nd
paragraph )
Briefly define the following:
Full time equivalent
Program cost factor
Weighted FTE
Base student allocation
District cost differential
Sparsity supplement
Supplemental academic instruction
0.748 Mills Discretionary Compresion (audio is incorrect-changed from Local Discretionary Equalization).
ESE guaranteed allocation
Required local effort
Please answer all in as a mini- brief and follow directions as I tried to be as spicific as possible with the questions.
.
Briefly compare and contrast EHRs, EMRs, and PHRs. Include the typic.docxCruzIbarra161
Briefly compare and contrast EHRs, EMRs, and PHRs. Include the typical content and functionality of each.
Focusing on one of these types of records, describe the key benefits for one of the stakeholders (e.g., patients, providers, or health care management) of being able to record and/or access patient data through this system.
Should all patient health information be recorded electronically? If so, explain why. If not, explain what the exceptions should be and why.
.
Brief Exercise 9-11Suppose Nike, Inc. reported the followin.docxCruzIbarra161
*Brief Exercise 9-11
Suppose
Nike, Inc.
reported the following plant assets and intangible assets for the year ended May 31, 2014 (in millions): other plant assets $954.9; land $226.7; patents and trademarks (at cost) $530.7; machinery and equipment $2,137.2; buildings $967; goodwill (at cost) $207.5; accumulated amortization $59.3; and accumulated depreciation $2,290.
Prepare a partial balance sheet for Nike for these items.
(List Property, Plant and Equipment in order of Land, Buildings and Equipment.)
NIKE, INC.
Partial Balance Sheet
As of May 31, 2014
(in millions)
[removed]
[removed]
$
[removed]
[removed]
$
[removed]
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:
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$
[removed]
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:
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*Exercise 9-7
Wang Co. has delivery equipment that cost $50,840 and has been depreciated $24,960.
Record entries for the disposal under the following assumptions.
(Credit account titles are automatically indented when amount is entered. Do not indent manually.)
(a)
It was scrapped as having no value.
(b)
It was sold for $37,200.
(c)
It was sold for $19,360.
No.
Account Titles and Explanation
Debit
Credit
(a)
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
(b)
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
(c)
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
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*Exercise 9-8
Here are selected 2014 transactions of Cleland Corporation.
Jan. 1
Retired a piece of machinery that was purchased on January 1, 2004. The machine cost $62,160 and had a useful life of 10 years with no salvage value.
June 30
Sold a computer that was purchased on January 1, 2012. The computer cost $37,000 and had a useful life of 4 years with no salvage value. The computer was sold for $5,630 cash.
Dec. 31
Sold a delivery truck for $9,310 cash. The truck cost $23,600 when it was purchased on January 1, 2011, and was depreciated based on a 5-year useful life with a $3,290 salvage value.
Journalize all entries required on the above dates, including entries to update depreciation on assets disposed of, where applicable. Cleland Corporation uses straight-line depreciation.
(Record entries in the order displayed in the problem statement. Credit account titles are automatically indented when amount is entered. Do not indent manually.)
Date
Account Titles and Explanation
Debit
Credit
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
(To record depreciation expense for the first 6 months of 2014)
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[removed]
[remo.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Course Title CS591-Advance Artificial Intelligence
1. Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Advanced Artificial Intelligence Assignment
Graduate project level 2
Abstract
Artificial Intelligence (AI) is a crucial technical technology that
is commonly used in today's
society. Deep Learning, in particular, has a variety of uses due
to its ability to learn robust
2. representations from images. A Convolutional Neural Network
(CNN) is a Deep Learning
algorithm which commands the input image, assigns
significance to numerous aspects/objects in
the image, and can distinguish between them. For image
classification, CNN is the most popular
Deep Learning architecture. To get better results, we used
various automated processing tasks for
fruit and vegetable images. In comparison to other classification
deep learning algorithms, the
amount of pre-processing needed by a CNN model is much
lower. Furthermore, the learning
capabilities of Deep Learning architectures can be used to
improve sound classification in order
to solve efficiency problems. CNN is used in this project, and
layers are created to classify the
sound waves into their various categories.
Introduction
We humans enjoy analyzing items, and everything you can think
of can be classified into a
classification or class. It is an everyday issue in business;
analysis of parts, installations,
gatherings, and products are necessary for the daily routine.
3. This is the reason why people have
devised procedures such as Machine Learning (ML), Neural
Networks (NN), and Deep Learning
(DL), among other calculations, to automate the arrangement
period. Deep learning will be one
of them that we will explore. Deep learning is an artificial
intelligence (AI) function that
simulates how the human brain processes data and creates
patterns to make decisions. The
classification of photographs of fruits and vegetables with the
naked eye is very difficult. As a
result, we're using pyTorch to process image datasets with Deep
Learning. We're developing a
CNN model for image detection and categorization using these
datasets. A custom CNN is
introduced and then compared to a ResNet CNN for the
purposes of this study. The other is
sound classification, in which we classify specific sounds and
measure their accuracy using
datasets given by ultrasound8k.
[1] Fruits, Vegetables and Deep Learning Processing Image
Datasets with Convolutional
Neural Networks using PyTorch
4. Description: Convolutional Neural Networks or Deep Learning
architectures were developed
form the inspiration of the human brain and how it process
information. CNN are a type of
Neural Network that provides good results in areas such as
image processing, image recognition
and image classification. This is the reason why, based on the
title of this piece, a CNN model is
required.
Convolutional Neural Networks are a branch of Deep Learning.
The human brain and how it
processes knowledge inspired the creation of Convolutional
Neural Networks. CNNs are a type
of Neural Network that performs well in image processing,
image recognition, and image
classification. This is why, as the title of this article suggests, a
CNN model is needed.
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
5. Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
CNNs are a form of artificial neural network that filters data
using convolutional layers for
learning purposes. To create a transformed image, input data
(feature map) is combined with a
convolution kernel (filter).
The input layer, hidden layers (which can range from 1 to the
number required by the
application), and output layer are the three main components of
a CNN. A CNN differs from a
normal Neural Network in that its layers are structured in three
dimensions (width, height, and
depth). Convolution, pooling, normalization, and completely
linked layers make up the hidden
layers.
To put it another way, a CNN is a Deep Learning algorithm that
6. can take images as input, inspect
them in various ways for patterns or artifacts, and then output
the ability to distinguish one from
another.
Steps:
vegetables using CNN and using
PyTorch library.
Question is to analyze
image classification. This dataset, which is available on Kaggle,
includes images of fruits
and vegetables with the following key characteristics:
Total number of images: 90483.
Training set size: 67692 images (one fruit or vegetabl e per
image).
Test set size: 22688 images (one fruit or vegetable per image).
Multi-fruits set size: 103 images (more than one fruit (or fruit
class) per image)
Number of classes: 131 (fruits and vegetables).
Image size: 100x100 pixels.
7. DataSet Size:700MB
sidebar in which you will find
add data option we have to click it and search for dataset
fruits360 and add it.
use GPU processor to execute
our models fastly. It is available in Kaggle for 40 hours to new
users. We also have make
sure the internet is on in Kaggle which is under settings.
code. We first have to load
the directory paths from the dataset and confirm that the
directory have a similar number
of classes. To conform we will display all classes in each
folders of the root directory and
images in some classes to test.
certifiable AI models, it is very basic to
part the dataset into 3 sections:
Training set: used to prepare the model for example process the
misfortune and change
loads of the model utilizing inclination drop.
8. Validation set: used to assess the model while preparing, change
hyperparameters
(learning rate, and so on), and pick the best form of the model.
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Test set: used to analyze various models, or various kinds of
demonstrating approaches,
and report the last exactness of the model.
remembered for the Training
9. catalog would be utilized as Training Dataset, and a similar will
be for the Test registry
as Validation Dataset. The Test set would the Validation
Dataset.
Data Augmentations" will
apply transformations at random. Specifically, each image will
be pad by 10 pixels before
being flipped horizontally with a 50% chance. Finally, a random
20-degree rotation will
be applied. Since the transformation will be applied randomly
and dynamically each time
a specific image is loaded.
ll work with a lot of
data. That data should be
handled by a PC, and PCs have restricted assets. It would be
inconceivable for a machine
to run every one of the 67692 pictures remembered for this
dataset without a moment's
delay. Consequently, you will require data loaders. Fortunately
PyTorch has them.
CNN . Let's define an
10. ImageClassificationBase class and an accuracy function for the
models before we get into
the specifics of each one.
model performs. Finding
the number of labels that were correctly predicted, or the
precision of the forecasts, is a
natural way to do this.
be built on
Residual Blocks and Batch
Normalization. This is so that the effects of the custom CNN
and the ResNet
model(ResNet stands for residual neural networks, which are
pre-trained models in in the
ImageNet dataset) can be compared. The original input is added
back to the output
feature map obtained by moving the input through one or more
convolutional layers by
Residual Block. Batch Normalization, as the name implies,
normalizes the convolutional
layers' inputs by taking them all to the same size. This cuts
down on the time it takes to
train the neural network.
11. Training the Custom CNN
Model. Then we have to do Training to the ResNet CNN Model
which is in a similar
fashion to the custom CNN model.
,Validation Loss ,Validation
Accuracy. The accuracy must be greater then 90% for our
models to use in predictions.
rained
models to make predictions. The
predictions would be identical since both models achieved
greater than 90% accuracy.
save option. So you can
review your work in future.
Source Code:
import os
import torch
import torchvision
import tarfile
12. Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from torchvision.datasets.utils import download_url
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader
import torchvision.transforms as tt
13. from torch.utils.data import random_split
from torchvision.utils import make_grid
import matplotlib.pyplot as plt
%matplotlib inline
from tqdm.notebook import tqdm
import torchvision.models as models
# Load the directory paths to the dataset
DATA_DIR = '../input/fruits/fruits-360'
TRAIN_DIR = DATA_DIR + '/Training'
TEST_DIR = DATA_DIR + '/Test'
# Look at the root directory
print('The folders inside the root directory are: ')
print(os.listdir(DATA_DIR))
# The classes are the name of the folders inside the Training
directory
train_classes = os.listdir(TRAIN_DIR)
print('nThe classes on the Training directory are: ')
14. print(train_classes)
print('The Training directory has %s classes.'
%len(train_classes))
# The classes are the name of the folders inside the Test
directory
test_classes = os.listdir(TEST_DIR)
print('nThe classes on the Test directory are: ')
print(test_classes)
print('The Training directory has %s classes. n'
%len(test_classes))
print('nThe images inside the /Training/Apple Red 2 directory
are:')
print(os.listdir(TRAIN_DIR + '/Apple Red 2'))
print('nThe /Training/Apple Red 2 directory has %s images.'
%len(os.listdir(TRAIN_DIR + '/A
pple Red 2')))
print('nThe images inside the /Test/Apple Red 2 directory are:')
print(os.listdir(TEST_DIR + '/Apple Red 2'))
print('nThe /Test/Apple Red 2 directory has %s images.'
%len(os.listdir(TEST_DIR + '/Apple R
15. ed 2')))
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
train_tfms = tt.Compose([tt.RandomCrop(100, padding=10,
padding_mode='reflect'),
tt.RandomHorizontalFlip(),
tt.RandomRotation(20),
tt.ToTensor()
17. batch_size_resnet = 32 # Batch size for resnet CNN model
random_seed = 42
torch.manual_seed(random_seed);
# DataLoaders for Custom CNN Model
train_dl_custom = DataLoader(train_ds, batch_size_custom,
shuffle=True, num_workers=3, pin
_memory=True)
valid_dl_custom = DataLoader(valid_ds, batch_size_custom*2,
num_workers=3, pin_memory=
True)
# DataLoaders for ResNet CNN Model
train_dl_resnet = DataLoader(train_ds, batch_size_resnet,
shuffle=True, num_workers=3, pin_m
emory=True)
valid_dl_resnet = DataLoader(valid_ds, batch_size_resnet*2,
num_workers=3, pin_memory=Tru
e)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpura m Chaitanya
18. sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
def show_batch(dl):
for images, labels in dl:
fig, ax = plt.subplots(figsize=(12, 12))
ax.set_xticks([]); ax.set_yticks([])
ax.imshow(make_grid(images[:64], nrow=8).permute(1, 2,
0))
break
print('train_dl_custom dataloader samples: ')
20. """Move tensor(s) to chosen device"""
if isinstance(data, (list,tuple)):
return [to_device(x, device) for x in data]
return data.to(device, non_blocking=True)
class DeviceDataLoader():
"""Wrap a dataloader to move data to a device"""
def __init__(self, dl, device):
self.dl = dl
self.device = device
def __iter__(self):
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
21. from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
"""Yield a batch of data after moving it to device"""
for b in self.dl:
yield to_device(b, self.device)
def __len__(self):
"""Number of batches"""
return len(self.dl)
device = get_default_device()
device
# Device Data Loader for Custom CNN Model
train_dl_custom = DeviceDataLoader(train_dl_custom, device)
valid_dl_custom = DeviceDataLoader(valid_dl_custom, device)
# Device Data Loader for Custom CNN Model
22. train_dl_resnet = DeviceDataLoader(train_dl_resnet, device)
valid_dl_resnet = DeviceDataLoader(valid_dl_resnet, device)
def accuracy(outputs, labels):
_, preds = torch.max(outputs, dim=1)
return torch.tensor(torch.sum(preds == labels).item() /
len(preds))
class ImageClassificationBase(nn.Module):
def training_step(self, batch):
images, labels = batch
out = self(images)
loss = F.cross_entropy(out, labels) # Calculate
training loss
return loss
def validation_step(self, batch):
images, labels = batch
out = self(images) # Generate
predictions
23. loss = F.cross_entropy(out, labels) # Calculate
validation loss
acc = accuracy(out, labels) # Calculate
accuracy
return {'val_loss': loss.detach(), 'val_acc': acc}
def validation_epoch_end(self, outputs):
batch_losses = [x['val_loss'] for x in outputs]
epoch_loss = torch.stack(batch_losses).mean() #
Combine losses
batch_accs = [x['val_acc'] for x in outputs]
epoch_acc = torch.stack(batch_accs).mean() #
Combine accuracies
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for discipli nary
action, including dismissal
24. from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
return {'val_loss': epoch_loss.item(), 'val_acc':
epoch_acc.item()}
def epoch_end(self, epoch, result):
print("Epoch [{}], last_lr: {:.10f}, train_loss: {:.4f},
val_loss: {:.4f}, val_acc: {:.4f}".format(
epoch, result['lrs'][-1], result['train_loss'],
result['val_loss'], result['val_acc']))
def conv_block(in_channels, out_channels, pool=False):
layers = [nn.Conv2d(in_channels, out_channels,
kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels), # Batch
Normalization
nn.ReLU(inplace=True)]
if pool: layers.append(nn.MaxPool2d(2))
return nn.Sequential(*layers)
25. class CustomCNN(ImageClassificationBase):
def __init__(self, in_channels, num_classes):
super().__init__()
self.conv1 = conv_block(in_channels, 128)
# 3 x 64 x 64
self.conv2 = conv_block(128, 256, pool=True)
# 128 x 32 x 32
self.res1 = nn.Sequential(conv_block(256, 256),
conv_block(256, 256)) # 256 x 32 x 32
self.conv3 = conv_block(256, 512, pool=True)
# 512 x 16 x 16
self.conv4 = conv_block(512, 1024, pool=True)
# 1024 x 8 x 8
self.res2 = nn.Sequential(conv_block(1024, 1024),
conv_block(1024, 1024)) # 1024 x 8 x 8
self.conv5 = conv_block(1024, 2048, pool=True)
# 256 x 8 x 8
self.conv6 = conv_block(2048, 4096, pool=True)
# 512 x 4 x 4
self.res3 = nn.Sequential(conv_block(4096, 4096),
26. conv_block(4096, 4096)) # 512 x 4 x 4
self.classifier = nn.Sequential(nn.MaxPool2d(4),
# 9216 x 1 x 1
nn.Flatten(), #
9216
nn.Linear(9216, num_classes))
# 131
def forward(self, xb):
out = self.conv1(xb)
out = self.conv2(out)
out = self.res1(out) + out # Residual Block
out = self.conv3(out)
out = self.conv4(out)
out = self.res2(out) + out # Residual Block
out = self.classifier(out)
return out
# remove the + out to see the differences of adding the
output at the end
27. Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
class ResNetCNN(ImageClassificationBase):
def __init__(self):
super().__init__()
# Use a pretrained model
self.network = models.resnet34(pretrained=True) # You
can change the resnet model her
e
28. # Replace last layer
num_ftrs = self.network.fc.in_features
self.network.fc = nn.Linear(num_ftrs, 131) # Output
classes
def forward(self, xb):
return torch.sigmoid(self.network(xb))
def freeze(self):
# To freeze the residual layers
for param in self.network.parameters():
param.require_grad = False
for param in self.network.fc.parameters():
param.require_grad = True
def unfreeze(self):
# Unfreeze all layers
for param in self.network.parameters():
param.require_grad = True
29. @torch.no_grad()
def evaluate(model, val_loader):
print('Evaluating Model ...')
model.eval()
outputs = [model.validation_step(batch) for batch in
tqdm(val_loader)]
return model.validation_epoch_end(outputs)
def get_lr(optimizer):
for param_group in optimizer.param_groups:
return param_group['lr']
def fit_one_cycle(epochs, max_lr, model, train_loader,
val_loader,
weight_decay=0, grad_clip=None,
opt_func=torch.optim.SGD):
torch.cuda.empty_cache()
history = []
# Set up cutom optimizer with weight decay
30. optimizer = opt_func(model.parameters(), max_lr,
weight_decay=weight_decay)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
# Set up one-cycle learning rate scheduler
sched = torch.optim.lr_scheduler.OneCycleLR(optimizer,
max_lr, epochs=epochs,
steps_per_epoch=len(train_loader))
31. for epoch in range(epochs):
# Training Phase
model.train()
train_losses = []
lrs = []
print('nTraining Model ...')
for batch in tqdm(train_loader):
loss = model.training_step(batch)
train_losses.append(loss)
loss.backward()
# Gradient clipping
if grad_clip:
nn.utils.clip_grad_value_(model.parameters(),
grad_clip)
optimizer.step()
optimizer.zero_grad()
# Record & update learning rate
33. custom_model = to_device(CustomCNN(input_channels,
output_classes), device)
custom_model
for images, labels in train_dl_custom:
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
print('images.shape:', images.shape)
out = custom_model(images)
print('out.shape:', out.shape)
36. Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
accuracies = [x['val_acc'] for x in history]
plt.plot(accuracies, '-x')
plt.xlabel('epoch')
plt.ylabel('accuracy')
plt.title(model_name + ' - Accuracy vs. No. of epochs');
def plot_losses(history, model_name):
train_losses = [x.get('train_loss') for x in history]
37. val_losses = [x['val_loss'] for x in history]
plt.plot(train_losses, '-bx')
plt.plot(val_losses, '-rx')
plt.xlabel('epoch')
plt.ylabel('loss')
plt.legend(['Training', 'Validation'])
plt.title(model_name + ' - Loss vs. No. of epochs');
def plot_lrs(history, model_name):
lrs = np.concatenate([x.get('lrs', []) for x in history])
plt.plot(lrs)
plt.xlabel('Batch no.')
plt.ylabel('Learning rate')
plt.title(model_name + ' - Learning Rate vs. Batch no.');
plot_accuracies(history_CustomCNN, 'Custom CNN Model')
plot_losses(history_CustomCNN, 'Custom CNN Model')
plot_lrs(history_CustomCNN, 'Custom CNN Model')
38. plot_accuracies(history_ResNetCNN, 'ResNet CNN Model')
plot_losses(history_ResNetCNN, 'ResNet CNN Model')
plot_lrs(history_ResNetCNN, 'ResNet CNN Model')
def predict_image(img, model):
# Convert to a batch of 1
xb = to_device(img.unsqueeze(0), device)
# Get predictions from model
yb = model(xb)
# Pick index with highest probability
_, preds = torch.max(yb, dim=1)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
39. discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
# Retrieve the class label
return valid_ds.classes[preds[0].item()]
img, label = valid_ds[2569]
plt.imshow(img.permute(1, 2, 0))
print('Label:', valid_ds.classes[label], ', Predicted:',
predict_image(img, custom_model))
img, label = valid_ds[9856]
plt.imshow(img.permute(1, 2, 0))
print('Label:', valid_ds.classes[label], ', Predicted:',
predict_image(img, custom_model))
Screenshots:
40. Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
41. Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
42. assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
43. StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
44. action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
45. Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
46. SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
47. assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
48. Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
49. Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
50. assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
51. sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
52. from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
53. Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
54. StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
55. work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
56. Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
57. Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
58. assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
59. StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
60. action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
61. Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
62. SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
63. assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
[2] Sound Classification using Deep Learning
Definition: Sound plays an important role in every aspect of
human life. Sound is a crucial
component in the development of automated systems in a
variety of fields, from personal
security to critical surveillance. While a few systems are
already on the market, their reliability is
a problem for their use in real-world scenarios. Recent advances
in image classification, where
convolutional neural networks are used to classify images with
high precision and at scale, raises
the question of whether these techniques can be applied to other
domains, such as sound
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
64. SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
classification. In this project, we are going to demonstrate how
deep learning is used for sound
classification.
Deep learning architectures' learning capabilities can be used to
build sound classification
systems that resolve the inefficiencies of traditional systems.
We created a sequential model with
the following specifications using the Keras library and
TensorFlow. The convolutional neural
network was a two-layer deep architecture with a completely
linked final layer and an output
prediction layer.
65. Some of the real world applications for deep learning are:
-degree protection and security
capabilities
Steps:
The steps for classifying sound using Deep Learning are as
follows:
1) Data Exploration and Visualisation
2) Data Pre-processing and Data Splitting
3) Model Training and Evaluation
4) Model Refinement
Description:
1) Data Exploring and Visualization:
The “Urbansound8K Dataset" will be used because the aim of
this Question is to analyze sound
classification. There are 8732 sound samples (=4s) of urban
sounds in the dataset, divided into
ten categories: They are
66. r Horn
patterns in the results. We'll
load the audio file into an array with librosa, then show the
waveform with
librosa.display and matplotlib. Here, we will also add the
urbansoundmetadata.csv file
into the panda frame.
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
67. StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
for each of the audio sample files, we'll extract the
number of audio channels,
sample rate, and bit-depth.
2) Data Pre-processing and Data Splitting:
-process the data to make
dataset consistent in audio
channels, sample rate and bit-depth.
remove complications of bit-
depths.
and store it in a Panda
68. Dataframe along with it's classification label and encode the
categorical text data into
model-understandable numerical data, using
sklearn.preprocessing.LabelEncoder
function.
sets by using
sklearn.model_selection.train_test_split function.
3) Model Training and Evaluation:
accuracy on both the training
and test data sets.
predictions on a particular
audio.wav file.
4) Model Refinement:
testing data is low. So, to improve
the accuracy we will be using Convolutional Neural Network
(CNN) in this step.
ors all the same size by zero.
corresponds to the number of
69. classifications that can be created. The model would then make
a prediction based on
which alternative has the best chance of succeeding.
convert our model back to a
Convolutional Neural Network (CNN and start training the
dataset with a small number
of epochs and a small batch size because training a CNN can
take a long time. If the
output indicates that the model is convergent, we can increase
both numbers.
accuracy increased by 6%
and testing accuracy increased by 4%.
ferent sounds that weren't
included in either our test or
training data to further validate our model.
Course Title: CS591-Advance Artificial Intelligence
70. StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Source Code:
import IPython.display as ipd
ipd.Audio('../UrbanSound Dataset sample/audio/100032-3-0-
0.wav')
# Load imports
import IPython.display as ipd
import librosa
71. import librosa.display
import matplotlib.pyplot as plt
# Class: Air Conditioner
filename = '../UrbanSound Dataset sample/audio/100852-0-0-
0.wav'
plt.figure(figsize=(12,4))
data,sample_rate = librosa.load(filename)
_ = librosa.display.waveplot(data,sr=sample_rate)
ipd.Audio(filename)
# Class: Car horn
filename = '../UrbanSound Dataset sample/audio/100648-1-0-
0.wav'
plt.figure(figsize=(12,4))
data,sample_rate = librosa.load(filename)
_ = librosa.display.waveplot(data,sr=sample_rate)
ipd.Audio(filename)
# Class: Children playing
73. Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
ipd.Audio(filename)
# Class: Drilling
filename = '../UrbanSound Dataset sample/audio/103199-4-0-
0.wav'
plt.figure(figsize=(12,4))
data,sample_rate = librosa.load(filename)
_ = librosa.display.waveplot(data,sr=sample_rate)
ipd.Audio(filename)
# Class: Engine Idling
filename = '../UrbanSound Dataset sample/audio/102857-5-0-
0.wav'
75. ipd.Audio(filename)
# Class: Siren
filename = '../UrbanSound Dataset sample/audio/102853-8-0-
0.wav'
plt.figure(figsize=(12,4))
data,sample_rate = librosa.load(filename)
_ = librosa.display.waveplot(data,sr=sample_rate)
ipd.Audio(filename)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
76. assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
# Class: Street music
filename = '../UrbanSound Dataset sample/audio/101848-9-0-
0.wav'
plt.figure(figsize=(12,4))
data,sample_rate = librosa.load(filename)
_ = librosa.display.waveplot(data,sr=sample_rate)
ipd.Audio(filename)
import pandas as pd
metadata = pd.read_csv('../UrbanSound Dataset
sample/metadata/UrbanSound8K.csv')
metadata.head()
print(metadata.class_name.value_counts())
# Load various imports
import pandas as pd
77. import os
import librosa
import librosa.display
from helpers.wavfilehelper import WavFileHelper
wavfilehelper = WavFileHelper()
audiodata = []
for index, row in metadata.iterrows():
file_name =
os.path.join(os.path.abspath('/Volumes/Untitled/ML_Data/Urba
n Sound/UrbanSo
und8K/audio/'),'fold'+str(row["fold"])+'/',str(row["slice_file_na
me"]))
data = wavfilehelper.read_file_properties(file_name)
audiodata.append(data)
# Convert into a Panda dataframe
audiodf = pd.DataFrame(audiodata,
columns=['num_channels','sample_rate','bit_depth'])
78. # num of channels
print(audiodf.num_channels.value_counts(normalize=True))
# sample rates
print(audiodf.sample_rate.value_counts(normalize=True))
# bit depth
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
81. mfccsscaled = np.mean(mfccs.T,axis=0)
except Exception as e:
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
print("Error encountered while parsing file: ", file)
return None
return mfccsscaled
82. # Load various imports
import pandas as pd
import os
import librosa
# Set the path to the full UrbanSound dataset
fulldatasetpath = '/Volumes/Untitled/ML_Data/Urban
Sound/UrbanSound8K/audio/'
metadata = pd.read_csv('../UrbanSound Dataset
sample/metadata/UrbanSound8K.csv')
features = []
# Iterate through each sound file and extract the features
for index, row in metadata.iterrows():
file_name =
os.path.join(os.path.abspath(fulldatasetpath),'fold'+str(row["fol
d"])+'/',str(row["sli
ce_file_name"]))
class_label = row["class_name"]
83. data = extract_features(file_name)
features.append([data, class_label])
# Convert into a Panda dataframe
featuresdf = pd.DataFrame(features,
columns=['feature','class_label'])
print('Finished feature extraction from ', len(featuresdf), ' files')
from sklearn.preprocessing import LabelEncoder
from keras.utils import to_categorical
# Convert features and corresponding classification labels into
numpy arrays
X = np.array(featuresdf.feature.tolist())
y = np.array(featuresdf.class_label.tolist())
# Encode the classification labels
le = LabelEncoder()
yy = to_categorical(le.fit_transform(y))
84. Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
# split the dataset
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X, yy,
test_size=0.2, random_state = 42)
### store the preprocessed data for use in the next notebook
%store x_train
85. %store x_test
%store y_train
%store y_test
%store yy
%store le
# retrieve the preprocessed data from previous notebook
%store -r x_train
%store -r x_test
%store -r y_train
%store -r y_test
%store -r yy
%store -r le
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.optimizers import Adam
86. from keras.utils import np_utils
from sklearn import metrics
num_labels = yy.shape[1]
filter_size = 2
# Construct model
model = Sequential()
model.add(Dense(256, input_shape=(40,)))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.5))
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
87. sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
model.add(Dense(num_labels))
model.add(Activation('softmax'))
# Compile the model
model.compile(loss='categorical _crossentropy',
metrics=['accuracy'], optimizer='adam')
# Display model architecture summary
model.summary()
# Calculate pre-training accuracy
89. # Evaluating the model on the training and testing set
score = model.evaluate(x_train, y_train, verbose=0)
print("Training Accuracy: ", score[1])
score = model.evaluate(x_test, y_test, verbose=0)
print("Testing Accuracy: ", score[1])
import librosa
import numpy as np
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
90. assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
def extract_feature(file_name):
try:
audio_data, sample_rate = librosa.load(file_name,
res_type='kaiser_fast')
mfccs = librosa.feature.mfcc(y=audio_data,
sr=sample_rate, n_mfcc=40)
mfccsscaled = np.mean(mfccs.T,axis=0)
except Exception as e:
print("Error encountered while parsing file: ", file)
return None, None
return np.array([mfccsscaled])
def print_prediction(file_name):
prediction_feature = extract_feature(file_name)
92. # Class: Street music
filename = '../UrbanSound Dataset sample/audio/101848-9-0-
0.wav'
print_prediction(filename)
# Class: Car Horn
filename = '../UrbanSound Dataset sample/audio/100648-1-0-
0.wav'
print_prediction(filename)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
93. submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
filename = '../Evaluation audio/dog_bark_1.wav'
print_prediction(filename)
filename = '../Evaluation audio/drilling_1.wav'
print_prediction(filename)
filename = '../Evaluation audio/gun_shot_1.wav'
print_prediction(filename)
# sample data weighted towards gun shot - peak in the dog
barking sample is simmilar in shape t
o the gun shot sample
filename = '../Evaluation audio/siren_1.wav'
print_prediction(filename)
# retrieve the preprocessed data from previous notebook
95. print("Error encountered while parsing file: ", file_name)
return None
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
return mfccs
# Load various imports
import pandas as pd
96. import os
import librosa
# Set the path to the full UrbanSound dataset
fulldatasetpath = '/Volumes/Untitled/ML_Data/Urban
Sound/UrbanSound8K/audio/'
metadata = pd.read_csv('../UrbanSound Dataset
sample/metadata/UrbanSound8K.csv')
features = []
# Iterate through each sound file and extract the features
for index, row in metadata.iterrows():
file_name =
os.path.join(os.path.abspath(fulldatasetpath),'fold'+str(row["fol
d"])+'/',str(row["sli
ce_file_name"]))
class_label = row["class_name"]
data = extract_features(file_name)
features.append([data, class_label])
97. # Convert into a Panda dataframe
featuresdf = pd.DataFrame(features,
columns=['feature','class_label'])
print('Finished feature extraction from ', len(featuresdf), ' file s')
from sklearn.preprocessing import LabelEncoder
from keras.utils import to_categorical
# Convert features and corresponding classification labels into
numpy arrays
X = np.array(featuresdf.feature.tolist())
y = np.array(featuresdf.class_label.tolist())
# Encode the classification labels
le = LabelEncoder()
yy = to_categorical(le.fit_transform(y))
# split the dataset
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X, yy,
98. test_size=0.2, random_state = 42)
import numpy as np
from keras.models import Sequential
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, Conv2D,
MaxPooling2D, GlobalAveragePooling2D
from keras.optimizers import Adam
101. # Calculate pre-training accuracy
score = model.evaluate(x_test, y_test, verbose=1)
accuracy = 100*score[1]
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
print("Pre-training accuracy: %.4f%%" % accuracy)
from keras.callbacks import ModelCheckpoint
102. from datetime import datetime
#num_epochs = 12
#num_batch_size = 128
num_epochs = 72
num_batch_size = 256
checkpointer =
ModelCheckpoint(filepath='saved_models/weights.best.basic_cn
n.hdf5',
verbose=1, save_best_only=True)
start = datetime.now()
model.fit(x_train, y_train, batch_size=num_batch_size,
epochs=num_epochs, validation_data=(x
_test, y_test), callbacks=[checkpointer], verbose=1)
duration = datetime.now() - start
print("Training completed in time: ", duration)
# Evaluating the model on the training and testing set
score = model.evaluate(x_train, y_train, verbose=0)
104. filename = '../UrbanSound Dataset sample/audio/100852-0-0-
0.wav'
print_prediction(filename)
# Class: Drilling
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
filename = '../UrbanSound Dataset sample/audio/103199-4-0-
0.wav'
106. Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
107. SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
108. assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
109. Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinar y
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
110. Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
111. assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
112. sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
113. from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
114. Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
115. StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
116. submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
117. Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
118. discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
119. Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
120. SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
121. assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Course Title: CS591-Advance Artificial Intelligence
StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara
SEMOID#S02023694
StudentEmail: [email protected]
Date:04/20/2021
Violations of academic honesty represent a serious breach of
discipline and may be considered grounds for disciplinary
action, including dismissal
from the University. The University requires that all
assignments submitted to faculty members by students be the
work of the individual student
submitting the work. An exception would be group projects
assigned by the instructor. (Source: SEMO website)
Conclusion:
122. The results indicate that the Custom Model produced better
results than the ResNet Model
implemented in the PyTorch module, even though training took
longer time. The Custom Model
was 99.21 percent accurate, while the ResNet Model was just
92.45 percent accurate. In contrast
to the ResNet Model, the Custom Model was able to reduce
training and validation losses.
The results of UrbanSound data indicate that our trained model
has a Training accuracy of
98.19% and a Testing accuracy of 91.92%.
References:
1. Aguilar, F. (2020, July 19). Fruits, Vegetables and Deep
Learning - Level Up Coding.
Medium. https://levelup.gitconnected.com/fruits-vegetables-
and-deep-learning-
c5814c59fcc9
2. Smales, M. (2021, February 12). Sound Classification using
Deep Learning - Mike
Smales. Medium. https://mikesmales.medium.com/sound-
classification-using-deep-
learning-8bc2aa1990b7
List of contributions: Every one of us worked in each aspect to
accomplish the task and meet
the given requirements so that each of us can get a clear idea of
123. the topic.
Demonstration Coding Documentation
Sasidhar Reddy Vajrala 25% 25% 25%
Namratha Valle 25% 25% 25%
Malemarpuram Chaitanya sai 25% 25% 25%
Nagendra Mokara 25% 25% 25%
https://levelup.gitconnected.com/fruits-vegetables-and-deep-
learning-c5814c59fcc9
https://levelup.gitconnected.com/fruits-vegetables-and-deep-
learning-c5814c59fcc9
https://mikesmales.medium.com/sound-classification-using-
deep-learning-8bc2aa1990b7
https://mikesmales.medium.com/sound-classification-using-
deep-learning-8bc2aa1990b7
GOAL: Collect all the details for the outline and expand into a
meaningful report containing the 3-4 items that will go into
your individual/group portfolio.
DELIVERABLE:
1. Front page containing - your name and period of study in CS
dept with the University logo
2. Complete index for the entire document
3. For each project, pls include material that is applicable:
· Name of Advisor / Professor under whom this project was
conducted
· Purpose and keywords (example 1 (Links to an external
site.) , example 2
· Related Work (Reference articles and video tutorials) about
state-of-art and explaining the merit of your work: 0.5 page
(minimum) for each project
· References - IEEE style for works referenced below.
· Procedure including algorithms and pseudocode: (2-5 pages
124. WITHOUT screenshots for each project)
· Original block diagrams and comparison schemes explaining
functionality of each block
· Pseudocode showing the main components of algorithm
· Link to your git repository (if applicable)
· (Pls avoid screenshots until and unless absolutely necessary)
Submission details:
· File type - PDF
· Font - 12 point, single spaced, 1-inch margin
· Margin:
· top and bottom: 1 inch
· left and right: 0.75 inch
GOAL: To prepare an OUTLINE of your GRADUATE-
level group portfolio.
DETAILS: A portfolio is a personalized collection of most
significant academic works. This can include various projects,
assignments, papers, presentations that were completed during
the student members' period of study Masters level.
DELIVERABLE: For each project, you need to answer the
following questions (at least 500 words):
1. What was the goal?
2. Motivation and purpose of the project
3. How did you solve the problem? What was the team members
contribution?
· Which algorithms used?
· What was the basis of the solution?
· Which language did you use? How to deploy your solution?
· Evaluation, Validation and Key results
· 4-5 keywords to describe your project
Submission details:
· Font - 12 point, single spaced, 1-inch margin
125. GOAL: ADDRESS feedback comments in midterm report.
SUBMIT final portfolio and exit interview questionnaire.
FINAL REPORT: For each project,
1. ADD the "
Abstract" from Portfolio Outline as an introductory
page for each of your projects.
·
Portfolio Outline
2. ADD 3-5 meaningful
keywords for each project.
3. EXTEND your
midterm portfolio: for each project, pls include material
that is applicable:
(2-5 pages for each project)
· Name of Advisor / Professor under whom this project was
conducted
· Who is the audience for your work in each project? Who will
find this most helpful? Why?
· Design diagrams - process and data flow
· presentable and useful - use icons, presentable colors, clear
fonts
· Evaluation, Validation and Key results
· How did you know that your project is "working"?
· Proof of work: screenshots showing working module
· Experimental results supporting your hypothesis/goal.
· Conclusion & Future work - how to take this work further?
Your recommendations to next generation of students for this
project?
· References - IEEE style (at least 5-7 each for every project)
4. INCLUDE
Index for the entire document with page numbers. Each
project should start from a new page.
126. Formatting details:
· File type - PDF
· Font size - 12 point, single spaced, 1-inch margin.
ADDITIONAL INSTRUCTIONS:
1. Consistency
· consistency in formatting and grammar (e.g. no personal
pronounces such as I, me, my, you.)
· consistency in content distribution
· consistency in student contribution
· consistency in preparedness