1D-CNN that predicts the direction of the EURUSD pair.
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Updated
Mar 31, 2024 - Jupyter Notebook
1D-CNN that predicts the direction of the EURUSD pair.
Human Activity Recognition from Smartphone Data using Machine Learning and Sequential Deep Learning Techniques
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).
Raw Audio End-to-End Deep Learning Architectures for Sound Event Detection
Hybrid Deep Learning Approach for Monthly Rainfall Prediction Using Endogenous property and global Climatic Indices
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.
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.
This repo contains code used to tackle Challenge I of AI Hackathon
Contains code for Adaptive protection platform in Smart grids
Kaggle solution. Public repository for my 8th place solution to the Parkinson's Freezing of Gait competition.
Impulse Classification Network (ICN) for video Head Impulse Test
Neural network -based thermal excess correction for resolved asteroid spectral radiances in near-infrared
Hyperparameter Optimization for 1D-CNN-Based Network Intrusion Detection Using GA and PSO
Biendata astradata competition 1st place solution. (https://www.biendata.com/competition/astrodata2019/)
Predict the type of arrhythmia based on Electro-cardiogram (ECG) tool using machine learning models and algorithms.
IDH and TERTp mutation classification in gliomas using 1D-CNN with MRS data.
An attempt to forecast the upcoming cases for CoVID19 in India using 1D-CNN, LSTM and BRNN based model . The dataset has been taken from the Kaggle Competition https://www.kaggle.com/covid19
Supported Models: MobileNet [V1, V2, V3_Small, V3_Large] (Both 1D and 2D versions with DEMO, for Classification and Regression)
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