1-dimensional convolution implementation using C++ and CUDA
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Updated
Jan 24, 2020 - C++
1-dimensional convolution implementation using C++ and CUDA
Speech Emotion Recognition using 1D and 2D Convolutional Neural Networks
Complete project of predicting the weather condition using Deep Learning
Model for translating speech to text. This is similar to using amazon alexa amongst many other products
DeepFake ECG generator based on 1D Convoloutional Neural Networks
This work is the preliminary experiments leading to the publication: Towards Invariant Soft Biometrics from Electrocardiograms
IDH and TERTp mutation classification in gliomas using 1D-CNN with MRS data.
This repository is associated with Multi-Label ECG Classification Using Convolutional Neural Networks in a Classifier Chain
Bengali Newses are classified in six catagories. This is done by first extracting the semantics of Bengali words using word2vec. Then using those semantics, all the news are classified. Bengali NLP resources are not very rich compared to other languages. This is a complete project that includes Bengali word embedding, data cleaning using word st…
On-the-fly spectrogram generation
A version of the YOLOv3 network capable of handling 1D data inputs.
A repository related to a master thesis in electronics, informatics and technology. Title: "Comparing Cardiological and Algorithm-Based ECG Interpretation in Athletes: Can Artificial Intelligence Improve the Algorithms?"
Public repository associated with: "Using deep convolutional neural networks to predict patients age based on ECGs from an independent test cohort"
1DConvNet applied to room occupancy detection based on data from several environment sensors. Data courtesy of the UCI Machine Learning Repository.
This repository contains Convolutional Neural Networks implemented from scratch.
1-D convolution implementation using Python and CUDA
NLP-FinHeadlines-MoodTracker is a NLP project utilising sentiment analysis on financial news headlines. It employs a combination of CNN and LSTM layers to predict sentiment (positive, negative, neutral). The model incorporates an embedding layer, 1D convolution, max pooling, bidirectional LSTM, dropout, and dense layer for sentiment classification.
Public repository associated with: "MemoryInception: Predicting Neurological Recovery from EEG Using Recurrent Inceptions"
[Re] Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL, N. Strodthoff, P. Wagner, T. Schaeffter, and W. Samek,
Anti-hydrogen detection using CNNs from ASACUSA experiment
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