MWDatasetV2

Phase 1 ----------------------------------- Dataset Folder Structure: datasets/│├── Readme.txt├── Phase 1 - Dataset/│ ││ ├── C4001 Reflected Signal Dataset│ │ ├── extracted_features_All.csv // combined│ │ └── All other materials CSV files Smoothed and extracted features //individuals│ ││ └── C4001 with ADS1115 Reflected Signal Dataset│ │ ├── extracted_features_All.csv // combined│ │ └── All other materials CSV files Smoothed and extracted features //individuals │ │ │ ││ └── GSM Reflected Signal Dataset│ │ ├── extracted_features_All.csv│ │ └── All other materials CSV files Smoothed and extracted features //individuals │ │ │ ││ └── GSM with ADS1115 Reflected Signal Dataset│ ├── extracted_features_All.csv│ └── All other materials CSV files Smoothed and extracted features //individuals ///////////////////////////////////////////////////////////////////////////////////////////// \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\How to use datasets: 1- Download the CSV files from this datasets. 2- Load the CSV files into your preferred programming environment(Python, MATLAB, R, Weka, etc.). 3- Select one or more datasets depending on your experiment needs: A- extracted_features_All → for general classification. 4- The label column shows the material type for each sample(e.g., Plastic). 5- Use the feature columns (Mean, min, max, etc.) as inputs to machine learning algorithms. 6- Split the dataset into training and testing sets (e.g., 80% / 20%) or use N-Folds Cross Validation. 7- Train your ML model and evaluate performance (accuracy, precision, recall). 9- Cite this dataset in your research/publication when using it./////////////////////////////////////////////////////////////////////////////////////////////////////// Phase 2 ---------------------------------------------- Dataset Folder Structure: datasets/│├── Readme.Phase2.txt├── Phase 2-Dataset/ │ ││ ├── C4001 Reflected Signal Dataset ----------> TrainingDataset and TestingDataset│ │ ├── extracted_features_All.csv // combined│ │ └── All other materials CSV files Smoothed and extracted features //individuals│ ││ └── C4001 with ADS1115 Reflected Signal Dataset ----------> TrainingDataset and TestingDataset│ │ ├── extracted_features_All.csv // combined│ │ └── All other materials CSV files Smoothed and extracted features //individuals │ │ │ ││ └── GSM Reflected Signal Dataset ----------> TrainingDataset and TestingDataset│ │ ├── extracted_features_All.csv│ │ └── All other materials CSV files Smoothed and extracted features //individuals │ │ │ ││ └── GSM with ADS1115 Reflected Signal Dataset ----------> TrainingDataset and TestingDataset│ ├── extracted_features_All.csv│ └── All other materials CSV files Smoothed and extracted features //individuals ///////////////////////////////////////////////////////////////////////////////////////////// \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\How to use datasets: 1- Download the CSV files from this datasets. 2- Load the CSV files into your preferred programming environment(Python, MATLAB, R, Weka, etc.). 3- Select one or more datasets depending on your experiment needs: A- extracted_features_All → for general classification. 4- The label column shows the material type for each sample(e.g., Material: Plastic, Type : 1 , Day: 2). 5- Use the feature columns (Mean, min, max, etc.) as inputs to machine learning algorithms. 6- Split the dataset into training and testing sets (e.g., 80% / 20%) or use N-Folds Cross Validation. 7- Train your ML model and evaluate performance (accuracy, precision, recall). 9- Cite this dataset in your research/publication when using it.///////////////////////////////////////////////////////////////////////////////////////////////////////