The document describes a project that aims to detect stress in IT professionals using image processing and machine learning. The proposed system uses a hybrid neural network of convolutional neural networks (CNNs) and support vector machines (SVMs) to classify stress levels based on facial expressions captured in images. The CNN is used to extract features from input images, which are then fed to the SVM for classification. The system has the goals of improving productivity and well-being of IT professionals by providing real-time feedback and interventions to manage stress. The document outlines the various modules, including user and admin modules, data preprocessing, and deep learning components. Architectural diagrams and screenshots of the system are also presented.

1. Stress analysis in IT Professionals using Image
Processing and Machine Learning
Project Co-ordinator
Mr.Kumaresan S
Assistant Professor
Department of Computer Science
and Engineering
Team Members:
Thinesh Prabaharan.D
Sunil Ranjith.T
Rubanraj.V
Sriakash.S
Under the Guidance of
Professor Dr.C.Srivenkateswaran
Department of Computer Science &
Engineering
Head of the Department
Dr. D. C. Jullie Josephine
Head of the Department
Computer Science & Engineering

2. INTRODUCTION
Deep Learning:
Deep learning is a subset of machine learning, which is essentially a neural network with
three or more layers.
 Deep learning algorithms run data through several “layers” of neural network algorithms,
• each of which passes a simplified representation of the data to the next layer.
Machine Learning
Machine learning is a branch of artificial intelligence (AI) which focuses on the use of
data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
There are four basic approaches: supervised learning , unsupervised learning , semi-supervised
learning , reinforcement learning.

3. MOTIVATION
• To detect stress in IT professionals by Real-time detection of
stress using facial expressions .
• To Improve productivity and well-being of IT professionals.
• To provide real-time feedback and interventions to manage stress.

4. • To detect stress in IT professionals by Real-time detection of
stress using facial expressions .
• To Improve productivity and well-being of IT professionals
• To provide real-time feedback and interventions to manage stress
OBJECTIVE

5. EXISTING SYSTEM
• The Existing System Machine Learning algorithms like KNN classifiers are
applied to classify stress.
• Image Processing is used at the initial stage for detection, the employee's
image is given by the browser which serves as input.
• In order to get an enhanced image or to extract some useful information
from it , image processing is used by converting image into digital form
and performing some operations on it.
• By taking input as an image and output may be image or characteristics
associated with that images.
• The emotion are displayed on the rounder box.
• The stress level indicating by Angry, Disgusted, Fearful, Sad.

6. PROPOSED SYSTEM
◦ The proposed System uses hybrid neural networks like CNN with SVM
classifiers, where CNN is used to extract features from the input images, which
are then fed to the SVM for classification.
◦ The SVM acts as the output layer of the CNN, taking the extracted features
and making a prediction for the class of the input image.
◦ Image Processing is used at the initial stage for detection, the employee's image is
given by the browser which serves as input.

7. LITERATURE SURVEY
S.No Paper Title Methodology Disadvantages
1.
Stress detection in IT Professional [2022] Image Processing and
Machine Learning
Even though KNN
classifier gives high
accuracy, it
is Computationally
Expensive.
2.
Systematic Stress Detection in
CNN Application[2021]
CNN Model Provide low accuracy
because of the audio
dataset.
3.
Stress and anxiety detection using
facial cues from videos[2017]
camera-based PPG signals can be
affected by noise such as
motion measurements.

8. MODULES
• User module
• Admin module
• Data preprocessing
• Deep learning
User Module
• The User can register the first. While registering he required a valid user email and mobile
for further communications
• The user then user can login into our system. First user has to run the ml model by clicking the run
button in user page.
• The python library will extract the features and appropriate emotion of the image.

9. Admin Module:
• The admin can login with his credentials.
• The admin can set the training and testing data for the project dynamically to the
code, He can view all users detected results in hid frame.
• The admin can also view the CNN model detected results from the user.
Data preprocessing
• Load the image dataset into memory and then Resize all the images to a fixed size so that they can
be fed into the CNN model.
• Split the dataset into training, validation, and testing sets. The training set is used to train the
model, and the testing set is used to evaluate the performance of the model.

10. Deep Learning
• We use deep hybrid neural networks like CNN with SVM classifiers, where CNN is used to
extract features from the input images, which are then fed to the SVM for classification.
• The SVM acts as the output layer of the CNN, taking the extracted features and making
a prediction for the class of the input image.
• In order to get an enhanced image or to extract some useful information from it, Image processing
is used by converting image into digital form and performing some operations on it.

11. ARCHITECTURAL DIAGRAM

12. GRAPH REPRESENTATION

14. SCREENSHOTS

17. CONCLUSION &RESULT
• The main goal of this research is to analyize and detect the stress using
stress detection machine learning model using image processing and a
combination of convolutional neural networks (CNNs) and support
vector machines (SVMs) specifically designed for IT professionals.
The proposed model utilizes real-time facial expressions to detect
stress, such as furrowed brows, tense jaw, and furrowed lips. The
proposed model has potential applications in the workplace for
monitoring employee stress levels and providing interventions to
improve workplace well-being and productivity. The proposed model
is non-invasive and can be integrated with existing workplace
technologies, making it an accessible and practical solution for stress
detection in IT professionals.

18. REFERENCES
◦ 1] B.V. Raju College , Bhimavaram ,"Stress detection in it professionals by image processing and
machine learning ",Vol 13 Issue 07,2022, ISSN:0377-9254
◦ [2] SS. K. Mohapatra, R. Kishore Kanna, G. Arora, P. K. Sarangi, J. Mohanty and P.
Sahu, "Systematic Stress Detection in CNN Application" , 2022, pp. 1-
4, doi : 10.1109/ICRITO56286.2022.9964761.
◦ [3] G. Giannakakis, D. Manousos, F. Chiarugi , “Stress and anxiety detection using facial
cues from videos,” Biomedical Signal processing and Control”, vol. 31, pp. 89- 101,
January 2017.
◦ [4]Nisha Raichur, Nidhi Lonakadi , Priyanka Mural, “Detection of Stress Using
Image Processing and Machine Learning Techniques”, vol.9, no. 3S, July 2017.
◦ [5]U. S. Reddy, A. V. Thota and A. Dharun, "Machine Learning Techniques for Stress
Prediction in Working Employees," 2018 IEEE International Conference on Computational
Intelligence and Computing Research (ICCIC), Madurai, India, 2018, pp. 1-4.
◦ [6] R. K and V. R. Murthy Oruganti, "Stress Detection using CNN Fusion," TENCON 2021 -
2021 IEEE Region 10 Conference (TENCON).

19. Thank You!