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Clinical risk prediction based on machine learning algorithms plays a vital role in modern healthcare. A crucial component in developing a reliable prediction model is collecting high-quality time series clinical events. In this work, we release such a dataset that consists of 22,588,586 Clinical... Read more

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● Work on the project “LANDSLIDE FORECASTING SYSTEM” with Dr. Varun Dutt, an Associate Prof of IIT MANDI. ● My task was to implement various Deep Learning Time Series Forecasting models and apply Ensemble Learning to improve prediction.

In this repository, based on the latest NLP pappers, we researched on sequential data and time series and developed tasks in NLP such as stock price prediction, time series prediction, sentiment analysis from text and We developed the language model and so on. This research is based on recurrent ... Read more

This script loads desired stock price training data, trains an XGBoost Regressor for Time Series Forecasting (allowing fine-tuning) and downloads the model to be used for prediction tasks.

The goal of this project was to predict stock market prices using a recurrent neural network. This project was inspired by the Standford paper "Financial Market Time Series Prediction with Recurrent Neural Networks Armando Bernal, Sam Fok, Rohit Pidaparthi December 14, 2012" . Using the Standford... Read more

Using CNN-LSTM deep learning model for digital soil mapping. This is the code for paper "Zhang et al. A CNN-LSTM model for soil organic carbon content prediction with long time series of MODIS-based phenological variables"

Stock prediction application

MicroAI™ is an AI engine that can operate on low power edge and endpoint devices. It can learn the pattern of any and all time series data and can be used to detect anomalies or abnormalities, make one step ahead predictions/forecasts, and calculate the remaining life of entities (whether it is i... Read more

The designed hybrid neural network model identifies optimal combinations of soil parameters and blends it with the rainfall pattern in a selected region to evolve the expectable crop yield. The backbone for the predictive analysis model with respect to the rainfall is based on the Time-Series app... Read more