Abstract: This paper presents MIS-LSTM, a hybrid framework that joins CNN encoders with an LSTM sequence model for sleep quality and stress prediction at the day level from multimodal lifelog data.

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 ...

A deep learning pipeline for de novo molecular generation using LSTM and reinforcement learning (REINFORCE), with support for fragment-based control and automated molecule evaluation. This project ...

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 ...

Abstract: Crop yield prediction is critical for agricultural insurance, better risk management, and efficient production strategies. In this study, we propose a novel machine-learning framework to ...

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 ...

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 ...

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 ...