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.
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Updated
Sep 25, 2023 - Jupyter Notebook
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.
1D-CNN that predicts the direction of the EURUSD pair.
IDH and TERTp mutation classification in gliomas using 1D-CNN with MRS data.
Hybrid Deep Learning Approach for Monthly Rainfall Prediction Using Endogenous property and global Climatic Indices
Human Activity Recognition from Smartphone Data using Machine Learning and Sequential Deep Learning Techniques
1 Dimensional Convolutional Neural Network for Iris dataset classification
Contains code for Adaptive protection platform in Smart grids
Biendata astradata competition 1st place solution. (https://www.biendata.com/competition/astrodata2019/)
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).
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.
Raw Audio End-to-End Deep Learning Architectures for Sound Event Detection
Heart Sound Segmentation And Classification | Kaggle Competition
This repo contains code used to tackle Challenge I of AI Hackathon
Classifier for detection and prediction of the type of MI or NORM from 12-lead ECG beats.
Neural network -based thermal excess correction for resolved asteroid spectral radiances in near-infrared
Impulse Classification Network (ICN) for video Head Impulse Test
Our project considers various machine learning and deep learning techniques like CNN and RNN based on free-text keystroke features for user authentication. Moreover, we will develop a simple UI to test new users.
Classification models 1D Zoo - Keras and TF.Keras
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