Closed AKSHITHA-CHILUKA closed 2 weeks ago
Thank you for submitting your pull request! 🙌 We'll review it as soon as possible. In the meantime, please ensure that your changes align with our CONTRIBUTING.md. If there are any specific instructions or feedback regarding your PR, we'll provide them here. Thanks again for your contribution! 😊
give me a sample how its look like, the current readme looks better.
give me a sample how its look like, the current readme looks better.
This project aims to implement a drowsiness detection system using deep learning techniques, comparing the performance of a convolutional neural network (CNN) with a Random Forest classifier for binary classification tasks. DROWSINESS DETECTION USING DEEP LEARNING
The dataset for this project can be accessed from the following link: https://figshare.com/articles/dataset/EEG_driver_drowsiness_dataset/14273687 Goal -This project aims to implement a drowsiness detection system using deep learning techniques, comparing the performance of a convolutional neural network (CNN) with a Random Forest classifier for binary classification tasks.
Dataset -The dataset for this project can be accessed from the following link: EEG Driver Drowsiness Dataset.
Libraries: Install the necessary libraries using pip: Setup and Installation -Python Environment: Ensure Python 3.x is installed on your system.
pip install tensorflow scikit-learn matplotlib
Numpy
Pandas
sklearn
Keras
Tensorflow
matplotlib
Libraries: Install the necessary libraries using pip: -pip install tensorflow scikit-learn matplotlib
Libraries used 1.Numpy 2.Pandas 3.sklearn 4.Keras 5.Tensorflow 6.matplotlib
Model Implementation Model 1 Architecture Description:
-Input Layer: Receives sequences of length 12 with one feature dimension. -Convolutional Blocks: Four blocks, each with two convolutional layers followed by element-wise addition (residual connection). Filters increase progressively (32, 64, 128, 256), ReLU activation. -Global Average Pooling Layer: GlobalAveragePooling1D layer computes the average of each feature map across all spatial locations. -Dense Layers: Four Dense layers (128, 90, 64, 32 neurons) with ReLU activation. -Output Layer: Single neuron with sigmoid activation for binary classification. Training Description:
-Optimizer: Stochastic Gradient Descent (SGD) with learning rate 0.001 and momentum 0.9. -Loss Function: Binary Crossentropy. -Regularization: No explicit regularization mentioned. -Normalization: No explicit normalization mentioned. Model 2 Architecture Description:
Training Description:
Training and Evaluation -Data Splitting: The dataset was split into training and testing sets using an 80:20 ratio. -Training: The CNN model was trained for 150 epochs with a batch size of 4. -Evaluation Metrics: Accuracy was used as the evaluation metric for both models.
Results To provide a conclusion based on the three models provided:
Model 2: Unlike the first model, Model 2 employs a simpler architecture with convolutional layers followed by pooling and dense layers. It lacks residual connections and global pooling, which may limit its ability to capture long-range dependencies in sequential data. However, it may still perform adequately for simpler tasks or datasets with less complex patterns.
Conclusion:
The choice of the best model depends on factors such as the complexity of the dataset, computational resources, and the specific requirements of the task at hand.
this look like chatgpt response to me.
This document now includes comprehensive details about the project's goals, dataset, setup, model architectures, and training processes, along with a clear comparison and conclusion about the models.