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
   139   140   141   142   143   144   145   146   147   148   149