About the AI Based Cough Recognition System project
AI Based Cough Recognition System brings artificial intelligence to everyday health monitoring. Health-related inputs are collected, processed by a trained model and turned into simple, easy-to-read insights.
In this build: Cough audio is converted into spectrogram or MFCC features and classified by a neural network.
ProjectNet offers AI Based Cough Recognition System as an Artificial Intelligence final year project for B.E / B.Tech (CSE, IT, AI & DS, AI & ML), BCA, MCA, M.Tech and B.Sc Computer Science. It comes with source code, a project report, a PPT and an installation guide, so you can set it up, understand every module and explain it confidently in your review and viva.
Problem statement
People often notice health changes too late, and regular check-ups are not always accessible. This project builds a low-cost, AI-assisted tool that screens inputs and gives early indications. It is meant for awareness and learning, not for diagnosis.
Existing system vs proposed system
Existing system
Most monitoring today is manual or relies on occasional visits to a doctor or vet, so gradual changes are easy to miss.
Proposed system
The proposed system collects health inputs, extracts features, applies a trained AI model and presents results and trends through a web interface.
Key features of AI Based Cough Recognition System
- AI-based analysis of health inputs
- Feature extraction and preprocessing
- Simple result view with trends over time
- User accounts and stored history
- Configurable thresholds and alerts
- Awareness-only guidance with clear disclaimers
System modules
| Module | What it does |
|---|---|
| Data collection | Takes user inputs, audio or sensor readings. |
| Preprocessing | Cleans, normalises and converts data into model-ready features. |
| AI model | Trains and runs the classifier or predictor. |
| Result and insights | Explains the outcome in plain language. |
| User management | Handles registration, login and history. |
| Dashboard | Shows trends and past results. |
How the system works
- The user registers and logs in.
- Health inputs or audio are provided.
- Data is cleaned and features are extracted.
- The model predicts or classifies.
- Insights are shown with a disclaimer.
- History is stored for future comparison.
Technology stack
Typical technologies used for Artificial Intelligence projects like this one. The installation guide lists the exact versions used in the build.
| Programming language | Python 3.x |
|---|---|
| Deep learning | TensorFlow / Keras (CNN and related models) |
| Data & image processing | OpenCV, NumPy, Pandas |
| Visualisation | Matplotlib, Seaborn |
| Application layer | Flask or Django with HTML, CSS and Bootstrap |
| Development tools | VS Code, Jupyter Notebook or Google Colab |
Hardware and software requirements
| Processor | Intel Core i3 / i5 or above (GPU optional; Google Colab can be used for training) |
|---|---|
| RAM | 8 GB recommended (4 GB minimum) |
| Storage | 10 GB free space for dataset, models and libraries |
| Operating system | Windows 10/11, Ubuntu or macOS |
| Software | Python 3.8+, pip, a modern browser (Chrome / Edge) |
Testing and evaluation
Accuracy, precision, recall and F1-score on test data are reported.
What you get with this project
- Complete source code with comments
- Project report (abstract, literature review, design, implementation, testing, conclusion)
- PPT for your review presentation
- Installation guide to run the project step by step
- Student support through WhatsApp / phone (+91 9688163291)
To order, message ProjectNet on WhatsApp or call +91 9688163291. After you confirm and pay through GPay, PhonePe or Paytm, the files are shared with you.
Applications and future scope
Applications
- Home health awareness
- Telemedicine support
- Wellness apps
- Veterinary monitoring
Future enhancements
- Wearable-device integration
- Mobile app
- Doctor-sharing option
Viva questions for AI Based Cough Recognition System
Be ready to answer these in your project review:
- Which features are extracted in AI based cough recognition system?
- Why did you choose this model?
- How is the data preprocessed?
- How do you evaluate the model?
- What are the ethical limits of an AI health tool?
- How can the system be improved?
Who can use this project?
AI Based Cough Recognition System is suitable for B.E / B.Tech (CSE, IT, AI & DS, AI & ML), BCA, MCA, M.Tech and B.Sc Computer Science. ProjectNet is based in Bangalore, Karnataka and helps students looking for final year projects in Bangalore and other cities, since all files are shared digitally.
Frequently asked questions
What is included with the AI Based Cough Recognition System project?
Every ProjectNet project is offered with the complete source code, a project report, a PPT and an installation guide.
How can I get the AI Based Cough Recognition System project?
Contact ProjectNet on WhatsApp at +91 9688163291 or call 9688163291 / 8270827832. After you confirm the project and make the payment through GPay, PhonePe or Paytm, the source code and report are shared with you.
What is the price of the AI Based Cough Recognition System project?
The listed price is ₹3,000. Please confirm the current price on WhatsApp before ordering.
Which students can use the AI Based Cough Recognition System project?
It is suitable for B.E / B.Tech (CSE, IT, AI & DS, AI & ML), BCA, MCA, M.Tech and B.Sc Computer Science. Use it as a reference to understand, learn from and extend.
Which technologies and software are needed for AI Based Cough Recognition System?
The typical stack is Python 3.x, TensorFlow / Keras (CNN and related models), OpenCV, NumPy, Pandas, Matplotlib, Seaborn. The installation guide lists the exact versions and steps.
Is AI Based Cough Recognition System meant for medical use?
No. It is an academic project for awareness and learning. Always consult a qualified doctor or veterinarian for medical advice.