C&EN

Research on Remaining Useful Life Prediction of PEMWE Stack Based on the LSTM-iTransformer Model

In the context of global energy shortages, traditional energy sources face issues of limited reserves and high prices. As a result, the importance of energy storage technology is increasingly ...
GitHub

mohansree14/Bayesian-Deep-Learning-for-Probabilistic-Financial-Forecasting

This project implements state-of-the-art deep learning models for financial time series forecasting with a focus on uncertainty quantification. The system provides not just point predictions, but ...
GEN

BindCraft AI Model Achieves One-Shot Functional Protein Design

For the field of drug development, hitting the right target with atomic precision to achieve therapeutic effect remains the core challenge. While traditional R&D pipelines are dependent on ...
Frontiers

A hybrid LSTM–transformer model for accurate remaining useful life prediction of lithium-ion batteries

With the widespread application of lithium-ion batteries in electric vehicles and energy storage systems, health monitoring and remaining useful life prediction have become critical components of ...
GitHub

NCRF++: An Open-source Neural Sequence Labeling Toolkit

Sequence labeling models are quite popular in many NLP tasks, such as Named Entity Recognition (NER), part-of-speech (POS) tagging and word segmentation. State-of-the-art sequence labeling models ...
IEEE

A Variational Inference-Based LSTM-Enhanced Deep Neural Model for Sequential Recommendations

Abstract: Long Short-Term Memory (LSTM) networks are particularly useful in recommender systems since user preferences change over time. Unlike traditional recommender models which assume static ...
Science Daily

Researchers use deep learning to predict flooding this hurricane season

The model was even able to reconstruct water levels for tide-gauge stations damaged by hurricanes, such as the station in Sandy Hook, New Jersey, which failed during Hurricane Sandy in 2012.
Scientific Research Publishing

Kulshreshtha, S., & Vijayalakshmi, A. (2020). An ARIMA-LSTM Hybrid Model for Stock Market Prediction Using Live Data. Journal of Engineering Science and Technology Review, 13 ...

ABSTRACT: The application of artificial intelligence in stock price forecasting is an important area of research at the intersection of finance and computer science, with machine learning techniques ...
Scientific Research Publishing

Kim, H., & Won, C. (2018). Forecasting the Volatility of Stock Price Index: A Hybrid Model Integrating LSTM with Multiple GARCH-Type Models. Computational Economics, 51, 29-49.

ABSTRACT: The application of artificial intelligence in stock price forecasting is an important area of research at the intersection of finance and computer science, with machine learning techniques ...