From 0c718f66252e9ca21562c2de8cb827e686e3d3ce Mon Sep 17 00:00:00 2001 From: Omar Santos Date: Fri, 8 Sep 2023 11:54:12 -0400 Subject: [PATCH] Update README.md --- ai_research/ethics_privacy/README.md | 40 +++++++++++++++++++++++----- 1 file changed, 34 insertions(+), 6 deletions(-) diff --git a/ai_research/ethics_privacy/README.md b/ai_research/ethics_privacy/README.md index f8310e7..7a304f4 100644 --- a/ai_research/ethics_privacy/README.md +++ b/ai_research/ethics_privacy/README.md @@ -54,11 +54,39 @@ - APIs and SDKs - Wireless transmission -- **Data Cleaning**: - - Pandas - - Sci-kit learn +### Data Cleaning: -- **Data Analysis**: - - TensorFlow and Keras - - Matplotlib and Seaborn +3. **Pandas**: + - **Example**: Cleaning a dataset with missing values using Pandas before training a machine learning model. + - **Relevant Link**: [Pandas Documentation](https://pandas.pydata.org/pandas-docs/stable/index.html) + - **Usage in HAR and AI**: Pandas can be used to structure and clean sensor data, making it suitable for training AI models capable of recognizing complex patterns in human activity data. + +4. **Sci-kit learn**: + - **Example**: Using Sci-kit learn for feature selection and removing irrelevant features from a dataset. + - **Relevant Link**: [Sci-kit learn Documentation](https://scikit-learn.org/stable/) + - **Usage in HAR and AI**: Sci-kit learn offers various tools for data preprocessing, which is a vital step in preparing data for AI algorithms, enhancing the performance of the models in HAR applications. + +### Data Analysis: + +5. **TensorFlow**: + - **Example**: Developing a deep learning model using TensorFlow to classify different activities based on sensor data. + - **Relevant Link**: [TensorFlow Documentation](https://www.tensorflow.org/learn) + - **Usage in HAR and AI**: TensorFlow provides a comprehensive platform for developing and training AI models capable of analyzing and recognizing patterns in human activity data. + +6. **Keras**: + - **Example**: Using Keras to create a convolutional neural network (CNN) for image recognition, an essential task in AI. + - **Relevant Link**: [Keras Documentation](https://keras.io/getting_started/intro_to_keras_for_engineers/) + - **Usage in HAR and AI**: Keras simplifies the process of building and optimizing neural networks, a crucial component in AI, to analyze human activity data more effectively and make predictions. + +### Visualization and Further Analysis: + +7. **Matplotlib**: + - **Example**: Using Matplotlib to visualize the distribution of different activities within a dataset. + - **Relevant Link**: [Matplotlib Documentation](https://matplotlib.org/stable/contents.html) + - **Usage in HAR and AI**: Visualization of data is essential in AI to understand underlying patterns and trends in data, aiding in the better development and tuning of models for HAR. + +8. **Seaborn**: + - **Example**: Creating a heatmap using Seaborn to visualize the correlation between different features in a dataset. + - **Relevant Link**: [Seaborn Documentation](https://seaborn.pydata.org/) + - **Usage in HAR and AI**: Seaborn can enhance data visualization in AI, assisting in identifying relationships and patterns in data which can influence the development and performance of HAR models.