Students Engagement Detection Using Hybrid EfficientNetB7 Together With TCN, LSTM, and Bi-LSTM (DAiSEE and VRESEE datasets)

project that analyses the sentiments of customers in a visually appealing way

PredictBay is an innovative project that aims to revolutionize decision-making in investment strategies through intelligent forecasting. Our platform utilizes advanced machine learning algorithms to provide accurate predictions for stocks from all over the world.

This is a special course made with DTU in collaboration with Silvi.ai. The aim is to benchmark DL models in the task of NER for biomedical papers.

It is simple project created using flask to predict the next word the user will write like on google search engine with the help of LSTM model

Comparative Analysis of Bi-Directional Long Short-Term Memory and BERT Models for Fake News Detection with Explainable AI Using Lime

Comparative Analysis of Bi-Directional Long Short-Term Memory and BERT Models for Fake News Detection with Explainable AI Using Lime

Classify Named Entities from text using Bi-directional LSTM model

End-to-end review sentiment classification with text preprocessing, Bidirectional Long Short-Term Memory networks and Glove embeddings.

Project the performance of three deep learning algorithms namely LSTM, Bi-LSTM, and GRU in performing clickbait news headline classification.

This is repository code of paper "Enhancing Multi-Output Time Series Forecasting with Encoder-Decoder Networks"

Semantic Enrichment, Data Augmentation and Deep Learning for Boosting Invoice Text Classification Performance: A Novel Natural Language Processing Strategy

Music Genre Classification with Turkish Lyrics

A custom autoscaler using Bi-LSTM model.

doctor_prescription_recognization_using_DeepLearning project for epics

Two neural networks to detect weapons and violence in videos

A comparison of vanilla LSTM model and bi-LSTM model for sentiment analysis.

Toxic Comment Classification using LSTM and Bi-LSTM

This repository contains the code for building a spam detection system for SMS messages using deep learning techniques in TensorFlow2. Three different architectures, namely Dense Network, LSTM, and Bi-LSTM, have been used to build the spam detection model. The final model has been deployed as a Streamlit app to showcase its working.