Detection of Stress in IT Employees using Machine Learning Technique | Python Final Year Project
Summary
TLDRThis video introduces a Python project for stress detection in IT employees using machine learning. The project, based on an IEEE 2022 conference paper, deviates from the original KNN classification approach by implementing a CNN model architecture. The model boasts a training accuracy of 87.34% and a validation accuracy of 98.45%. It includes a MySQL database, user registration and approval processes, live camera image capture, and an admin panel for stress assessment. The video demonstrates the system's functionality and highlights the performance analysis with metrics like accuracy, precision, recall, and F-measure, along with a dynamic confusion matrix.
Takeaways
- 📘 The video introduces a Python project for stress detection in IT employees using machine learning techniques, based on an IEEE 2022 conference paper.
- 🔍 The original paper proposes using KNN classification for stress detection, but the project in the video implements a CNN model architecture instead.
- 🎯 The CNN model achieved a training accuracy of 87.34% and a validation accuracy of 98.45%, indicating high performance on the validation set.
- 🛠️ Before executing the project, viewers are advised to install necessary libraries and ensure the correct version of Python is used, as well as attach a database using MySQL administrator.
- 📸 The project involves capturing images from a webcam for testing facial expressions, and it's important to adjust image sizes for optimal recognition.
- 🌐 The project includes a web interface accessible via a URL, featuring a homepage, registration, login, and admin functionalities.
- 🔑 There is a registration process for new users, which includes validation criteria for the password to ensure security.
- 👤 The admin has the ability to approve new user registrations, which is a crucial step before users can log in and use the system.
- 🖼️ Users can upload images to the system, and the model predicts the emotional state of the individual in the image, such as happy, angry, or neutral.
- 📊 The project provides performance analysis with metrics like accuracy, precision, recall, F-measure, and a confusion matrix to evaluate the model's performance.
- 📈 A dynamic chart is included to visually represent the classification results, updating in real-time as new images are uploaded and classified.
Q & A
What is the main topic of the video?
-The main topic of the video is a Python project for stress detection in IT employees using machine learning techniques.
What machine learning technique was originally proposed in the paper discussed in the video?
-The original paper proposed using the KNN (K-Nearest Neighbors) classification technique for stress detection.
What model architecture does the video suggest implementing instead of KNN?
-The video suggests implementing a CNN (Convolutional Neural Network) model architecture instead of KNN.
What are the reported training and validation accuracies of the CNN model mentioned in the video?
-The reported training accuracy of the CNN model is 87.34%, and the validation accuracy is 98.45%.
What is the first technical requirement mentioned in the video for executing the project?
-The first technical requirement mentioned is to install the necessary libraries on the required version of Python as specified in the requirements file.
How does the project handle the database attachment?
-The project handles database attachment by using MySQL administrator, going to the restore option, and restoring from a specified database file in the project's database folder.
What is the process for an employee to register in the system as described in the video?
-The employee must fill out a registration form with details such as username, password, email, mobile number, login ID, address, company, and state. The registration must also satisfy password validation criteria.
What validation is in place for the password during registration?
-The password must contain at least one number, one lowercase letter, one uppercase letter, one special symbol, and be between 6 to 10 characters long.
Why might a newly registered employee be unable to log in immediately after registration?
-A newly registered employee might be unable to log in immediately because their account must first be approved by an admin.
What are the two main parts or entities of the project mentioned in the video?
-The two main parts or entities of the project are the employee part, where employees upload their images, and the admin part, where the stress of the employees is identified.
How does the project categorize the different expressions for stress detection?
-The project categorizes expressions into classes such as angry, discourse, fearful, happy, neutral, sad, and surprised.
What performance analysis parameters are mentioned in the video?
-The performance analysis parameters mentioned are accuracy, precision, recall, F-measure, and a confusion matrix.
How does the video demonstrate the dynamic nature of the project's performance analysis?
-The video demonstrates the dynamic nature by showing how uploading a new image and getting a new classification result updates the chart in real-time.
Outlines
📚 Introduction to the Stress Detection Project
This paragraph introduces a Python project focused on detecting stress in IT employees using machine learning techniques. The project is based on an IEEE 2022 conference paper and proposes a Convolutional Neural Network (CNN) model instead of the K-Nearest Neighbors (KNN) classification mentioned in the original paper. The model boasts a training accuracy of 87.34% and a validation accuracy of 98.45%. The video script guides viewers on setting up the project environment, including installing necessary libraries and attaching a database using MySQL administrator. It also instructs on how to take sample images using a web camera and compressing them for better performance.
🔑 Registration and Login Process for Employees and Admins
The second paragraph explains the registration and login process for both employees and admins within the project. It details the registration fields required for new employees and the validation rules for the password, emphasizing the need for a strong password. After registration, new employees must be approved by an admin before they can log in. The admin panel includes features to approve user registrations and view registered employee details. The paragraph also describes the employee's ability to upload images for stress detection after successful login and authentication.
🖼️ Image Upload and Stress Detection Results
This paragraph demonstrates the process of uploading images by employees and the subsequent stress detection analysis performed by the admin. It explains how employees upload images, which are then analyzed by the admin to determine the emotional state of the employee, such as happy, angry, or neutral. The script includes a walkthrough of uploading different images and observing the predicted results in the admin panel. It also mentions the potential inaccuracies of the model and the importance of not expecting 100% accuracy.
📊 Performance Analysis and Dynamic Chart Update
The final paragraph discusses the performance analysis of the stress detection model, including metrics such as accuracy, precision, recall, F-measure, and a confusion matrix. It also introduces a dynamic chart that updates in real-time according to the classes predicted by the model. The script shows how uploading different images affects the chart's representation of the predicted emotional states. The paragraph concludes with a demonstration of logging out and a summary of the project's purpose, which is to detect stress in IT employees using machine learning techniques.
Mindmap
Keywords
💡Machine Learning
💡CNN Model Architecture
💡Stress Detection
💡KNN Classification
💡Training Accuracy
💡Validation Accuracy
💡MySQL Administrator
💡Web Camera
💡Image Compression
💡Registration and Login
💡Admin Approval
💡Confusion Matrix
Highlights
Introduction of a Python project for stress detection in IT employees using machine learning techniques.
The paper presented at an IEEE 2022 conference proposes a novel concept for stress detection.
The authors used KNN classification for stress detection, but the project will implement a CNN model architecture.
The model achieved a training accuracy of 87.34% and a validation accuracy of 98.45%.
Instructions on installing required libraries and setting up the database for the project.
Guide on attaching the database using MySQL administrator and restoring from a backup file.
The necessity of capturing sample images with a webcam for testing the model.
Demonstration of resizing and compressing images for optimal model performance.
Execution of the project using the 'stress.py' script and accessing it through a web browser.
Overview of the project's user interface, including registration, login, and admin functionalities.
Registration process validation, including password strength requirements.
Explanation of the approval process for new employee registrations by the admin.
Demonstration of the live camera feature for real-time image capture.
Process for uploading images and receiving stress detection results.
Admin panel functionality to review and approve employee registrations and view stress detection results.
Discussion of the model's accuracy and the potential for incorrect predictions.
Performance analysis section covering accuracy, precision, recall, F-measure, and confusion matrix.
Interactive chart that dynamically updates based on the predicted stress classes.
Final summary and acknowledgment of the project's demonstration and its contributions to stress detection in IT employees.
Transcripts
welcome viewers if still you're not
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this JP info Channel
hi in this video we are going to see
about a python project which is n title
as
detection of stress in it employees
using machine learning technique which
is an IEEE 2022 conference paper
so in this paper the authors have
proposed a concept of detecting the
stress of an I.T employee so
here the authors have used the KNN
classification for finding the stress
detection of an I.T employee instead
we are going to
Implement our model as CNN model
architecture so our proposed algorithm
or model is CNN model architecture
instead of the k n n which is mentioned
in the base paper so our training
accuracy of this model is
87.34 percentage and the validation
accuracy is of
98.45 percentage
so in order to see the execution of the
project before executing the project
make sure that your satisfy all the
requirements that is mentioned in the
requirements file by installing the
libraries on the required version of the
Python first thing is like you wanted to
attach the database so just go to mySQL
administrator for attaching the database
as this project consists of database
so just go to mySQL administrator and
then go to the restore option just click
the open backup file option now go to
the project source code location
and go to the database folder and select
the database and then click Start
restore
now the ray the restore operation was
finished successful message is being
displayed means the database has been
attached successfully in the MySQL now
you can close this MySQL part
the first thing is like copy the source
code location
go to the command prompt
now go to the source code location
so now we have go to the source code
location now before executing the
project make sure that you have taken
some sample of your images from your
camera so let me go to my camera so
first things like you need to connect
your web camera in case if you are
executing this project in your in your
PC in case if it does laptop make sure
that you have the web camera and you can
open this app so this is this is not the
project this is generally that is
available in your camera or PC that is
your laptop or PC the first thing is
like take some sample images for testing
the expression part
so now I have taken three pictures from
my web camera
so here you can see those pictures that
I have taken right now
if the image is very bigger size kindly
make sure that
to reduce the size of the image
so if it is very big
there may be some problem in
identifying the expression so just let
me compress the picture size
so let me make it somewhat smaller
and save it
same let me do it for other two images
also
so now let me execute the project so now
you can type python
stress.py after going to the source code
location and click enter
and after some time you can see this URL
just copy this URL and go to the browser
any of the browser I'm going to Firefox
and just paste this URL in the browser
part and then click enter and now you
can see the home screen and it comes to
the project the project title direction
of stress the 90 employees using machine
learning technique so this is the
home page of the project so there you
can see register login and admin part so
here we have two entities that is the
employee part as admin part so employee
part is the part where the employee will
be uploading their images and admin part
is the part who identifies the stress of
the employee so first thing is like
there should be a valid employee for
that purpose so whenever a new employee
has been registered it is it will be
approved only by that mean after that
only they can able to login into their
system so let me show you that execution
part the first thing is let's go to the
registration path so first new user
should register their details so here
you can see username password email
mobile number login id address company
and state so the important thing of this
registration page is like here we have
validation for the password part in case
if you are giving a smaller password it
will be not registering let me show you
that so first thing is like let me enter
the username as JP let me give the
password as JP
let me give my email ID
mobile number login ID
address
company
state
so now let us see what happens for this
details and click submit
so now you can see it has been the same
register paid and if you scroll down the
page you can see password should contain
at least one number one lawyer case
character one uppercase character one
special symbol and must be between six
two six six to ten characters long so
this is the validation that we have done
so it should satisfy this condition The
password should satisfy this condition
so now let me enter
password satisfying this condition
so now I have entered the details and
click submit
now you can see you have registered
successfully you proceed for the login
so only if you get this message the the
registration is success and click ok so
now it will be navigated to the login
part so now the registration is success
but even the registration is Success if
we are entering the correct username and
password also
you can see a message like this
incorrect username password please login
with current details because
any employee which is who are
registering the for the first time
should be approved by the Admin so first
let me go to the admin part so let me
open this in a new tab so this is the
admin page so this consists of default
username and password as admin and
so just enter the same user detail for
the admin and admin as username and
password as admin and I mean and click
login
you can see the login as access and
click ok
and once the login is successful if you
be navigated to the admin home page
where you can see the admin home page
where you can see the
e-register details so in the register
details you can see the details of the
users who have been registered that is
the employees of the register so here
you can see the user name JP which I
have done right now with the details of
the username email ID Mobile company
name State and everything and status you
can see it has been waiting so only if
the admin is been approved this this
user then only the user employee can
able to login into their system so let
me click approve
so now you can see the status has been
updated to the uproot part now let me go
to the user login page
now let me enter the correct username
and password and then click login
and now you can see it has been
successful it will be navigated to the
upload image part so if once after the
user
login is successful that is
authentication is successful you can see
the in the user part upload live camera
logout so this live camera is just to
check out the
their images in the live camera for the
user part this is not the main Consular
project so first let me click this live
camera and show you make sure that the
camera is connected in your PC or laptop
so here you can see the user details
that is the user camera
so this is the user part this is not the
main concept this is an additional
concept so let me go to the upload part
so here you the user that is employee
should upload their image so I have
already taken three sample images right
so let me upload one of the image and
then click upload
so once after the image is uploaded
again it will be navigated to the upload
image part now let me go to the admin
part and check so let me log into the
admin in the new tab and I'm checking
out it
so now after the admin login just go to
the user details part so in the user
detail part you can see the details of
the thing that is the user ID is two the
username is JP and the email ID the
company name what is the image that is
and what is the printed result of it so
the printed result is happy
so now let me upload some other image
and check what happens and let me upload
this image and then click upload
so now the image is uploaded again it
will navigate it to the upload image
part now let me go to the next tab that
is in the admin part and Let me refresh
the user details part
so now here you can see the again the
user the username JP with the details of
it which I have uploaded now it has
shown the predicted result is angry
so let me show with some other image
then click upload in the user part
and now it is uploaded and now let me go
to the admin part of it and let me
refresh it the user details
so now you can see the third image that
is a neutral one so in this way you can
check with the your own images or you
can check with the data set images also
so let me now we will see about the
other classification results other than
them that we have taken from the web
camera so here in the base paper you can
see the
other classes as angry discourse fearful
happy
neutral
sad and surprised
so we have already uploaded our live
images using happy angry and you tell us
being detected now let us see about all
other data set image so let me log in to
the user part and let me upload some
other images and check with it
we have already provided you the
the test images that we have to be
uploaded with these classes also so just
go to the test folder image of the
project
so in the test data you can find these
angry discus fearful happy neutral sad
and surprise folders where you can just
upload any of the image and then upload
it
so now if I refresh this page in admin
side
so here you can see the image and the
predicted result is angry so
let me check with the discuss part
and upload it
now let me refresh it
so here you can see the printed result
is discussed
and let me check with the other class
with fear
and upload it
in the admin part the printed result
part you can see it is printed as
neutral
so the fear part is not predicting
correctly
kindly note that any model or this
particular model is not 100 accurate so
there may be some
wrong calculations of it so here fear
has not been printed correctly so now
let me check with uploading some other
image
so let me check with this happy image
so what does character correctly
predicting that the classification of
this employee is happy
now let me go for the other part the
neutral part
load
and in the
admin side you can find it is predicting
as neutral
okay in the sad part
and click upload
and in the user details you can find
that it has been rated as sad
and the final classification just
surprise
and click upload
now let me refresh in admin part
so now you can find the printed result
is surprise so I have shown you with the
all the classification of the
things that is discard so you can just
uh make your own images from your webcam
and check whether this this and never
let me move to the next part that is
performance analysis part so in the
performance analysis part we will be
having performance analysis parameters
of accuracy precision value recall value
and F measure value and
comes the confusion Matrix with the
true table and treated variable where
you can find with the classes of angry
discuss few happy neutral sad and
surprise of it
and final comes the chat part and this
chart is a dynamic chart it will be
showing according to the
the classes that has been predicted so
here you can see there is too angry one
discussed two happy three neutral one
sad and one surprise so for example let
me upload other surprise
so let me upload this surprise image
so now if I
go to the admin part
and now let me check the user details
so here you can see that I have uploaded
this image now it is surprise and in the
graph part
the chart part you can find that now you
can see it has been updated so according
to the printed result this chart will be
varied
and now let me log out
and this is all about the project
detection of stress in it employees
using machine learning technique and
thank you all for watching
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