Page 144 - AI Ver 3.0 class 10_Flipbook
P. 144
Problem statement template for the above scenario is as follows:
Our High School Community Who
Has a problem that inefficient recycling and increased landfill waste What
the issue arises in common areas such as cafeterias, hallways,
When/while Where
classrooms, and outdoor spaces
An ideal solution would be an AI-powered system to automatically identify and sort waste Why
Stage 2 Data Acquisition
The foundation of any AI model lies in acquiring relevant and accurate data.
• Data Collection: Gather images of different types of waste items, such as paper, plastic bottles, food scraps,
and general trash.
• Sources: Collect data using cameras installed near waste bins or manually label images collected by students
for training.
• Data Types: Images (photos of waste items) with labelled categories (recyclable, compostable, trash).
Stage 3 Data Exploration
Understanding the data is crucial to building an effective model.
• Analyse the Data: Visualise the collected images and check if there are clear differences between categories
like plastic, paper, food waste, and non-recyclables.
• Insights: Use data visualisation to understand which waste type is often confused, such as compostable versus
non-compostable items, and identify the features that distinguish them.
Stage 4 Modelling
Designing the model requires choosing the right algorithms and training approaches.
• Algorithm selection: Choose a machine learning algorithm suitable for image classification, such as a
Convolutional Neural Network (CNN), which is effective in processing and recognising images.
• Model building: Develop different models using labelled images to train the AI to recognise and classify waste
correctly.
Stage 5 Evaluation
Thorough evaluation ensures the reliability and accuracy of the AI model.
• Test models: Test multiple models to find which one distinguishes between various types of waste with the
highest accuracy.
• Validation: Test the model with new images to see if model can correctly identify items in real-time and sort
them into the appropriate bins.
• Improvement: If the model struggles with certain items, add more training data or adjust the algorithm
parameters to improve accuracy.
Stage 6 Deployment
Deploying the model brings the AI system into real- world application.
• Model Integration: Integrate the model into a system where a camera and display screen are installed above
waste bins. The system automatically suggests which bin to use for each item based on camera detection.
142 Touchpad Artificial Intelligence (Ver. 3.0)-X

