NASA Space Apps Challenge Hackathon
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Updated
Oct 7, 2024 - Python
NASA Space Apps Challenge Hackathon
Conceptual Python code for 1D-CNN / LSTM / LightGBM for time series dataset (but actual codes)
1D CNN for flood prediction
Classification models 1D Zoo - Keras and TF.Keras
Network intrusion detection with Machine Learning (Deep Learning) experiment : 1d-cnn, softmax, neural networks, convolution
This repository contains code related to identifying malicious sensor nodes using the SensorNetGuard Dataset. The code implements three models: Long Short-Term Memory(LSTM), Gated Recurrent Unit(GRU) and One-Dimensional Convolutional Neural Network (1D-CNN).
1D-CNN that predicts the direction of the EURUSD pair.
A build-from-scratch 1D CNN language model used on patient's discharge summary phenotyping and comparing the LM with concept extraction based classification models.
Implementation of a multi-task model for encrypted network traffic classification based on transformer and 1D-CNN.
Myocardial Infarction Detection
PyTorch Implementation of "Understanding and Learning Discriminant Features based on Multiattention 1DCNN for Wheelset Bearing Fault Diagnosis" by Wang et al.
Implemented Divide and Conquer-Based 1D CNN approach that identifies the static and dynamic activities separately. The final stacked model gave an accuracy of 93% without the test data sharpening process.
Hyperparameter Optimization for 1D-CNN-Based Network Intrusion Detection Using GA and PSO
This research study employs a mixed-methods approach to analyze the global growth of Nigerian music, utilizing data from Spotify, UK Charts, and the Billboard Hot 100. Various data analysis techniques like descriptive statistics and sentiment analysis are applied, alongside predictive models like 1D CNN and Decision Trees.
Neural network -based thermal excess correction for resolved asteroid spectral radiances in near-infrared
Kaggle solution. Public repository for my 8th place solution to the Parkinson's Freezing of Gait competition.
Raw Audio End-to-End Deep Learning Architectures for Sound Event Detection
Time-series forecasting with 1D Conv model, RNN (LSTM) model and Transformer model. Comparison of long-term and short-term forecasts using synthetic timeseries. Sequence-to-sequence formulation.
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