DL-Simplified
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abhisheks008
Earthquake Prediction using DL
This model is used to predict earthquakes. Predicting earthquakes is one of the great unsolved problems in the earth sciences.
Project Title : Earthquake Prediction Aim : This model is used to predict earthquakes. Predicting earthquakes is one of the great unsolved problems in the earth sciences. Dataset: [Earthquake prediction] https://www.kaggle.com/datasets/taghreedaburwished/earthquake-prediction-dataset Approach : This model is developed using Recurrent Neural Network (RNN), Long Short-Term Memory Networks (LSTM), Matplotlib.
Gather information like past earthquakes, fault lines, and geological data. Select which data points might link to earthquakes, like patterns in seismic activity or changes in the earth. Use machine learning to identify connections between the data and when earthquakes happen. Try out the model with new data to see how well it predicts earthquakes. We can use svm,decision tree @abhisheks008
Gather information like past earthquakes, fault lines, and geological data. Select which data points might link to earthquakes, like patterns in seismic activity or changes in the earth. Use machine learning to identify connections between the data and when earthquakes happen. Try out the model with new data to see how well it predicts earthquakes. We can use svm,decision tree @abhisheks008
As this project repository mainly focuses on deep learning methods, you need to implement deep learning methods for this problem statement as well.
Got it I will try my best please assign it
Can you share the models you are planning to implement here?
Sir we will use svm naive Bayes random forest and linear regression
Sir we will use svm naive Bayes random forest and linear regression
Machine learning models will not work here, can share your approach based on deep learning models.
Use deep learning models to automatically extract meaningful features from raw seismic data. Techniques like convolutional neural networks (CNNs) can analyze spectrograms or waveforms, while recurrent neural networks (RNNs) can capture temporal dependencies in time series data.
Use deep learning models to automatically extract meaningful features from raw seismic data. Techniques like convolutional neural networks (CNNs) can analyze spectrograms or waveforms, while recurrent neural networks (RNNs) can capture temporal dependencies in time series data.
What are the specific architectures you are planning to implement for this problem statement?
@abhisheks008, I can start working on it, after making sure you approve my solution for the Micromobility-Lane-Recognition Issue
Full name: Basma Mahmoud GitHub Profile Link: Basma2423 Email ID: [email protected]
Approach for this Project:
- Data Loading and Preprocessing
- EDA
- Models: 3.1 Multiple Deep Learning approaches suitable for tabular data, e.g: FNN, TabNet, and Entity Embeddings for Categorical Variables. 3.2 Some pre-trained models, e.g. Pretrained TabNet, PyCaret, and AutoGluon.
- Models Assessment.
What is your participant role? (Mention the Open Source program): GSSoC-2024 participant
Can you add the label for GSSoC, please? Thanks.
Ok I will do data loading eda fnn and then model assessment
Can you share the finalized models for this problem statement?
@abhisheks008, I can start working on it, after making sure you approve my solution for the Micromobility-Lane-Recognition Issue
Full name: Basma Mahmoud GitHub Profile Link: Basma2423 Email ID: [email protected]
Approach for this Project:
- Data Loading and Preprocessing
- EDA
- Models: 3.1 Multiple Deep Learning approaches suitable for tabular data, e.g: FNN, TabNet, and Entity Embeddings for Categorical Variables. 3.2 Some pre-trained models, e.g. Pretrained TabNet, PyCaret, and AutoGluon.
- Models Assessment.
What is your participant role? (Mention the Open Source program): GSSoC-2024 participant
Can you add the label for GSSoC, please? Thanks.
Your previous contributor was in talks for this issue, you can check out other issues.
i will use SOCR earthquake dataset
I asked you about the finalized models for this problem statement.