Results 81 to 90 of about 9,795 (189)

From video to behaviour: An LSTM‐based approach for automated nest behaviour recognition in the wild

Methods in Ecology and Evolution, Volume 17, Issue 7, Page 2073-2081, July 2026.
Abstract Studies of animal behaviour usually rely on direct observations or manual annotations of video recordings. However, such methods can be very time‐consuming and error‐prone, leading to sub‐optimal sample sizes. Recent advances in deep learning show great potential to overcome such limitations.
Liliana R. Silva, André C. Ferreira, Irene Martínez‐Baquero, Arlette Fauteux, Claire Doutrelant, Rita Covas   +5 more
wiley   +1 more source

Remaining useful life prediction of lithium-ion batteries using a CEEMDAN–Bi-LSTM hybrid model

International Journal of Electrochemical Science
Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is a critical task in battery management systems. To address the nonlinearity and nonstationarity of raw capacity-fading data, as well as the fluctuation disturbances induced
Huanhuan Li, Yangyang Cao, Xiangfei Ren, Dewang Zhang, Tong Xu, Zhijie Su, Yunqi Jiang, Yaping Wang, Boyan Chen, Houzhong Zhang, Lei Pei, Zhen Wang, Yan Gao   +12 more
doaj   +1 more source

Short-term power load forecasting using informer encoder and bi-directional LSTM [PDF]

E3S Web of Conferences
An innovative model called InE-BiLSTM is proposed here, which combines the Informer Encoder with a bidirectional LSTM (Bi-LSTM) network. The goal is to enhance the precision and efficacy of short-term electricity load forecasting. By integrating the long-
Tan Shiyu, Yang Yuhao, Zhang Yongxin
doaj   +1 more source

Empowering Urdu sentiment analysis: an attention-based stacked CNN-Bi-LSTM DNN with multilingual BERT

Complex & Intelligent Systems
Sentiment analysis (SA) as a research field has gained popularity among the researcher throughout the globe over the past 10 years. Deep neural networks (DNN) and word vector models are employed nowadays and perform well in sentiment analysis.
Lal Khan, Atika Qazi, Hsien-Tsung Chang, Mousa Alhajlah, Awais Mahmood   +4 more
doaj   +1 more source

Smart grid forecasting method based on data preprocessing and Bi-LSTM

Diance yu yibiao
Short-term prediction plays an important role in the construction of smart grid, and profoundly affects the intelligent transformation of all aspects of power grid generation, transmission and distribution.
LI Yan, LIU Xinyue, QIAO Junjie, WANG Maotao, LIU Yifan, QI Leijie   +5 more
doaj   +1 more source

Enhancing Stock Price Forecasting Accuracy Using LSTM and Bi-LSTM Models [PDF]

ITM Web of Conferences
Accurately predicting stock price trends is of critical importance in the financial sector, enabling both individuals and enterprises to make informed and profitable decisions.
Wang Hao
doaj   +1 more source

Advanced Solar Irradiance Forecasting Using Hybrid Ensemble Deep Learning and Multisite Data Analytics for Optimal Solar-Hydro Hybrid Power Plants

International Transactions on Electrical Energy Systems
Solar energy with hydropower power plants marks a significant leap forward in renewable energy innovation. The combination ensures a consistent power supply by merging the fluctuations of solar energy with the predictable storage provided by hydropower ...
Sudharshan Konduru, Naveen C., Jagabar Sathik M.   +2 more
doaj   +1 more source

Assessment of the impact of climate change on streamflow of Ganjiang River catchment via LSTM-based models

Journal of Hydrology: Regional Studies
Study region: Ganjiang River catchment, China. Study focus: Quantifying the effects of the impacts climate change on streamflow is of great importance for regional water resources management. In this study, four LSTM-based models, i.e., LSTM, Stack-LSTM,
Chao Deng, Xin Yin, Jiacheng Zou, Mingming Wang, Yukun Hou   +4 more
doaj   +1 more source

Bi-LSTM Model for Emotion Recognition

Abstract- Emotion recognition can facilitate communication between humans and machines and also helps to enhance decision making process. There are many machine learning models which is used to recognize emotions from the text. To Upgrade human-machine interaction, a deep learning (DL) based approach is proposed.
openaire   +1 more source

Classification of cyber attacks in IoMT networks using deep learning: a comparative study

Journal of Computer Sciences Institute
The Internet of Medical Things (IoMT) is transforming healthcare through enhanced remote monitoring and real-time data exchange, but it also introduces significant cybersecurity challenges.
Asif Rahman Rumee
doaj   +1 more source