Class-9 Projects

Objective: Teach students about real-time object detection using AI.
Project: Build a system that detects and tracks objects (like a ball or a person) in real-time using AI algorithms and cameras.
Tools: Raspberry Pi with a camera module, Python with OpenCV for image processing and object detection, TensorFlow Lite for on-device AI.
Outcome: The AI-powered system can detect and track objects in real-time, showing how AI is applied in surveillance and autonomous robots.

Objective: Explore AI’s role in autonomous driving.
Project: Create a small autonomous car that uses AI for path planning and obstacle avoidance. The car can navigate through a track using sensors and AI decision-making.
Tools: Raspberry Pi, Arduino, sensors (ultrasonic for obstacle detection), motor drivers, and AI algorithms for decision-making.
Outcome: The car can navigate around obstacles autonomously, giving students insight into how self-driving vehicles function using AI.

Objective: Introduce students to AI applications in healthcare.
Project: Build a system that monitors vital signs (heart rate, temperature) and uses AI to analyze the data for early detection of health issues.
Tools: Raspberry Pi, health monitoring sensors (heart rate, temperature), Python with AI libraries for data analysis.
Outcome: The system collects health data and provides feedback or alerts based on AI analysis, teaching students how AI can assist in personal health monitoring.

Objective: Show students how AI can predict outcomes in sports based on data.
Project: Collect data on players or teams, such as past performance, and use AI to predict the outcome of upcoming matches.
Tools: Laptops/tablets, Python with machine learning libraries (scikit-learn), data sets from past sports events.
Outcome: The AI model makes predictions about future matches, showing students how predictive analytics works with AI.

Objective: Teach students about AI’s role in speech recognition.
Project: Build a voice assistant that can recognize commands and respond accordingly (e.g., controlling devices or answering questions).
Tools: Raspberry Pi with a microphone, Python with speech recognition libraries (like Google Speech-to-Text), and an AI-based NLP (Natural Language Processing) framework.
Outcome: Students build a system that responds to voice commands, showing how AI-powered voice assistants like Siri or Google Assistant function.

Objective: Introduce students to AI in security systems.
Project: Build a smart surveillance system that uses facial recognition and motion detection to monitor and report unusual activity.
Tools: Raspberry Pi with a camera, OpenCV for facial recognition, and machine learning models for detecting unusual movement.
Outcome: The system identifies faces and alerts when unknown faces or unusual movements are detected, helping students understand how AI is applied in security.

Objective: Teach students about chatbots and their practical applications in customer service.
Project: Create a chatbot that can handle customer queries, book appointments, or provide product recommendations based on user input.
Tools: Python with chatbot frameworks (ChatterBot, Rasa), or use drag-and-drop platforms like IBM Watson or Dialogflow.
Outcome: The chatbot can interact with users, simulating a customer service assistant, demonstrating how businesses use AI to automate customer support.

Objective: Show how AI can help in agriculture.
Project: Train an AI model to classify different types of crops or detect plant diseases based on images captured by a camera.
Tools: Raspberry Pi with a camera module, TensorFlow for image classification, and a dataset of plant images.
Outcome: The AI system identifies plant species or diseases, providing insights into AI applications in precision agriculture.

Objective: Teach students how AI is used in financial markets.
Project: Build a predictive model that analyzes past stock market data to forecast future trends.
Tools: Laptops/tablets, Python with libraries like Pandas, NumPy, and machine learning algorithms (scikit-learn or TensorFlow).
Outcome: The AI model predicts stock price trends, demonstrating how financial institutions use AI to make decisions.

Objective: Teach students about energy efficiency and AI.
Project: Build a system that monitors energy consumption and uses AI to optimize it by suggesting when to turn off devices or appliances.
Tools: Raspberry Pi, energy monitoring sensors, Python with machine learning algorithms for trend analysis.
Outcome: The system tracks energy usage and offers optimization suggestions, showing how AI can contribute to sustainability and energy conservation.