BILSTM, GMDH and Genetic COVID Forecasting Into Desired Steps of Future

Implementation of a deep learning model (BiLSTM) to detect code-switching

A PyTorch implementation of the BI-LSTM-CRF model.

reference pytorch code for named entity tagging

Sentiment Analysis on covid-19 crisis in Malaysia.

Comparitive analysis of image captioning model using RNN, BiLSTM and Transformer model architectures on the Flickr8K dataset and InceptionV3 for image feature extraction.

PredictBay aims to revolutionize decision-making in investment strategies through intelligent forecasting. Our platform utilizes advanced machine learning algorithms to provide accurate predictions for stocks .

This project improves information retrieval by detecting duplicate question pairs in the Quora dataset using data exploration, text preprocessing, feature engineering, and models like Random Forest and LSTM, aiming to streamline question-answering.

This project is dedicated to forecasting 1-hour EURUSD exchange rates through the strategic amalgamation of advanced deep learning techniques. The incorporation of key technical indicators—RSI, MA, EMA, and VWAP—enhances the model's grasp of market dynamics

My Deep Learning Course Research Project

This project is part of my Master's thesis for the MSc in Data Science with Artificial Intelligence program at the University of Exeter. It implements a multi-task deep learning model for simultaneous driver identification and transport mode classification using smartphone sensor data from the SHL preview dataset.

Web-based sentiment analysis app using BiLSTM and Attention models for text sentiment classification.

Comprehensive study on fake news detection using CNN, LSTM, and BiLSTM models with the LIAR dataset. Explore our implementation, results, and comparisons with existing research.

Molecular Property Prediction via LSTM and BiLSTM

This research investigates flight delay trends, examining departure time, airline, and airport factors. Regression machine learning meth- ods are utilized to predict delay contributions from various sources. Time-series models, including LSTM, Hybrid LSTM, and Bi-LSTM, are compared with baseline regression models such as Multiple Regression, Decisi

Modified version of RusStress (https://github.com/MashaPo/russtress) — python package for placing stress in Russian text using RNN (BiLSTM) and the "Grammatical Dictionary" by A. A. Zaliznyak (from http://odict.ru/).

Codes related to the paper "Attention-Based CNN-BiLSTM for Sleep States Classification of Spatiotemporal Wide-Field Calcium Imaging Data"

ExpressNet is a ready-to-use, weightless text classification model architecture that you can import and start training immediately.

Use Deep Learning (LSTM and BiLSTM) to predict the top 3 most profitable stocks in KLSE Bursa with an MAPE of less than 5%.

NLP project to Emotion Classification through your text