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1. How your group should proceed to develop the AI solution? (i.e. What are the five steps of machine learning?)

2. What kind of AI concepts are involved?

3. What would be some expected obstacles and possible ways to improve the solution?

4. What ethical considerations you should be aware of?

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1. How your group should proceed to develop the AI solution? (i.e. What are the five steps of machine learning?)

2. What kind of AI concepts are involved?

3. What would be some expected obstacles and possible ways to improve the solution?

4. What ethical considerations you should be aware of?

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1. How your group should proceed to develop the AI solution? (i.e. What are the five steps of machine learning?)

2. What kind of AI concepts are involved?

3. What would be some expected obstacles and possible ways to improve the solution?

4. What ethical considerations you should be aware of?

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These are my thoughts

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1. problem definition: developing a AI solusion to recognize the plants in EDUHK with teachable machine.

2. Data collection: taking 50 pictures of different plants, each plants with at least 5 pictures from different angles.

3. Data preprocessing: classifying the plants with picture and names, and croping, shearing the pictures to create more data.

4. Model training: Using the teachable machine to train the model to recognize the pictures of plants.

5. inference and predictions： using new pictures to test.

6. AI concepts: supervised learning.

7. Obstacles： similar plants cannot recognize. solution: Taking more pictures.

8. ethical consideration: the copyright of pictures online, damaging the plants when taking pictures, poisonous mushrooms maybe taken mistakenly.

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Developing a system to identify red flowers and hibiscus with Teachable Machine and Dancing with AI

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Developing a system to identify red flowers and hibiscus with Teachable Machine and Dancing with AI

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1.     Five Steps of Machine Learning:

l  Data Collection: Gather a dataset of images of the two types of flowers we want to recognize. This dataset should include various images under different lighting conditions, angles, and backgrounds to ensure robustness.

l  Data Preparation: Preprocess the images by resizing them to a uniform size, normalizing pixel values, and augmenting the dataset with techniques like rotation, flipping, and color adjustment to increase diversity. And label the images accurately with the corresponding flower names.

l  Model Selection: Convolutional Neural Networks (CNNs), which are effective for this task.

l  Training the Model:

Split the dataset into training, validation, and testing sets.

Train the model using the training set while monitoring performance on the validationset. Adjust hyperparameters as needed to optimize performance.

l  Evaluation and Deployment:

Evaluate the model’s performance using accuracy on the test set.

Once satisfied with the performance, deploy the model into an application where users can upload images of flowers and receive information about them.

2.

l  Image Processing: Techniques for handling and manipulating image data to improve model performance.

l  Deep Learning: Specifically using neural networks, particularly CNNs for image classification tasks.

l  Computer Vision: The field concerned with how computers can be made to gain understanding from digital images or videos.

3.

l  Data Quality and Quantity: Insufficient or poor-quality images can lead to a poorly performing model.

Improvement: Increase the dataset size and quality through data augmentation and sourcing more images.

l  Overfitting: The model may perform well on training data but poorly on unseen data.

Improvement: Use techniques like regularization, dropout, and cross-validation to mitigate overfitting.

l  Environmental Variability: Different lighting conditions and backgrounds can affect recognition accuracy.

Improvement: Train with a diverse set of images to make the model robust against variations.

4. Ethical Considerations:

l  Privacy: Ensure that users’ images are handled with care, and consider implementing policies on data retention and user consent.

l  Environmental Impact: Ensure that the application encourages conservation and respect for nature rather than promoting the collection of plants.

l  Misinformation: Be cautious about the accuracy of the information provided about plants, as incorrect data can lead to misuse or harm.

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Problem Statement

Identify various types of plants and provide detailed information about each.

Data Collection

Capture plant images (data augmentation techniques).

Compile data from a plant encyclopedia for reference.

Data Processing

Format the collected data for consistency and usability.

Model Training

Use the K-Nearest Neighbors (KNN) algorithm for classification.

Inference & Prediction

Test the tool by allowing users to upload images of different plants.

AI Concepts

Implement KNN for classification tasks.

Challenges

The model may struggle to recognize plants at different growth stages.

Ethical Considerations

Address copyright issues related to the use of information and images.

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1. Problem Definition: Provide a relationship between the traffic situations and deciding factors such as date, time, etc, and use that relationship to predict future traffic.

Data Collection: Gather historical data on inbound and outbound traffic at the control points. This data may include:

- Daily traffic counts

- Seasonal trends

- Holiday schedules

- Weekend patterns

- External factors (e.g., weather conditions, special events)

Data Processing: Clean and preprocess the data to ensure it is suitable for analysis. This may involve:

- Handling missing values

- Normalizing data

- Creating features that capture seasonal effects or trends

Model Training: Choose an appropriate model, such as linear regression. Plug in the data and perform the machine learning.

Inferences and Prediction: Use this model to predict future traffic, which can help future decisions on allocate resources for the traffic.

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1. Develop the AI to recognize the flowers around our campus by taking photos.

2. take pictures of flowers around the campus as many as possible and as many angles as possible, as more detailed as possible.

3. classify the flowers by colors , shape, size, petals,names, medical function

4. teachable machine

5. new pics and similar flowers pics to predict

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Regression concept is used because Regression analysis provides a quantitative framework for predicting traffic volumes based on historical data. By analyzing past traffic patterns, regression models can identify trends and relationships between various factors, such as time of year, day of the week, and special events, which are crucial for accurate forecasting.

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We may have chances to face the exposure of private information and we need to be careful to the personal privacy while collecting data. The solution for privacy protection is using the encryption technology and anonymization.