This project implements a word-level language model using an LSTM network in TensorFlow. The model is trained on "Poirot Investigates" (~53,000 words) and is capable of generating text in a style ...

Abstract: In recent studies, transfer learning has shown significant potential in enhancing model adaptability across diverse domains. This study presents a novel approach to cross-domain sequence ...

This paper explores the integration of Artificial Intelligence (AI) large language models to empower the Python programming course for junior undergraduate students in the electronic information ...

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 ...

Landslides are one of the most prevalent natural geological disasters, causing significant economic losses, damaging public environments, and posing severe threats to human lives. Landslide ...

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 ...

ABSTRACT: Accurate precipitation forecasting is crucial for mitigating the impacts of extreme weather events and enhancing disaster preparedness. This study evaluates the performance of Long ...

Long Short-Term Memory (LSTM) is a type of recurrent neural network architecture designed to address the vanishing gradient problem in traditional RNNs. LSTMs are particularly effective for time ...