Results 31 to 40 of about 3,794 (184)

Forecasting Cryptocurrency Prices Using LSTM, GRU, and Bi-Directional LSTM: A Deep Learning Approach

Fractal and Fractional, 2023
Highly accurate cryptocurrency price predictions are of paramount interest to investors and researchers. However, owing to the nonlinearity of the cryptocurrency market, it is difficult to assess the distinct nature of time-series data, resulting in ...
Phumudzo Lloyd Seabe, Claude Rodrigue Bambe Moutsinga, Edson Pindza   +2 more
doaj   +1 more source

Oilseed Rape Sclerotinia in Hyperspectral Images Segmentation Method Based on Bi-GRU and Spatial-Spectral Information Fusion

智慧农业
ObjectiveThe widespread prevalence of sclerotinia disease poses a significant challenge to the cultivation and supply of oilseed rape, not only results in substantial yield losses and decreased oil content in infected plant seeds but also severely ...
ZHANG Jing, ZHAO Zexuan, ZHAO Yanru, BU Hongchao, WU Xingyu   +4 more
doaj   +1 more source

Bi-GRU Based Deception Detection using EEG Signals

CoRR
Deception detection is a significant challenge in fields such as security, psychology, and forensics. This study presents a deep learning approach for classifying deceptive and truthful behavior using ElectroEncephaloGram (EEG) signals from the Bag-of-Lies dataset, a multimodal corpus designed for naturalistic, casual deception scenarios.
Danilo Avola, Muhammad Yasir Bilal, Emad Emam, Cristina Lakasz, Daniele Pannone, Amedeo Ranaldi   +5 more
openaire   +2 more sources

TAMNet: Temporal and adaptive‐frequency network with MixStyle for cross‐region oil and fluid production forecasting

Deep Underground Science and Engineering, EarlyView.
This paper presents temporal and adaptive‐frequency network with MixStyle (TAMNet), a deep time‐series modeling framework for accurate and robust multi‐well oil productivity forecasting. TAMNet integrates transformer and long short‐term memory architectures to capture both short‐ and long‐term temporal dependencies, enhanced by a temporal gate unit ...
Chunxi Yang, Leifeng Meng, Qinghu Fan, Ziang Guo, Ting Bao, Pingdong Zhong, Fulin Luo   +6 more
wiley   +1 more source

A Multi-Head Attention ResCNN–BiGRU Model for Robust SOC Estimation in EVs Lithium-Ion Batteries Using Real-World Driving Data

IEEE Access
Precise assessment of the state of charge (SOC) is essential for ensuring the safety, efficiency, and dependability of electric vehicles (EVs). However, achieving high accuracy remains challenging due to the complex nonlinear characteristics of lithium ...
N. Vigneswar, R. Manivannan, Chala Merga Abdissa   +2 more
doaj   +1 more source

Tree‐Boost–Guided CNN–BiLSTM–Transformer for Solar Irradiance Forecasting: Cross‐Regional Evidence for Sustainable Energy Planning

Energy Science &Engineering, EarlyView.
This graphical abstract illustrates a reproducible pipeline that combines gradient‐boosting‐based feature selection with a CNN–BiLSTM–Transformer model to forecast solar irradiance across multi‐site satellite and ground datasets, delivering robust, high‐accuracy predictions that support sustainable grid planning and reliable PV integration.
Muhammad Farhan Hanif, Usama Hassan, Javid Hussain, Irfan Ali Khan, Yuldasheva Gulora, Syed Masood Arif Bukhari   +5 more
wiley   +1 more source

Snow Depth Retrieval Using Detrended SNR From GNSS-R With Bidirectional GRU

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Snow depth monitoring is crucial for hydrology, climate research, and avalanche prediction. While traditional global navigation satellite system (GNSS) reflectometer methods offer cost-effective snow thickness retrieval, they suffer from poor accuracy ...
Wei Liu, Zihui Lin, Yuan Hu, Aodong Tian, Xintai Yuan, Jens Wickert   +5 more
doaj   +1 more source

Landslide Displacement Prediction Based on Transfer Learning and Bi-GRU

Journal of Sensors, 2022
Predicting slope deformation prediction is crucial for early warning of slope failure, preventing damage to properties, and saving human lives. However, in practice, equipment maintenance causes discontinuity in the displacement data, and the traditional prediction models based on deep networks do not perform well in this case.
Haiqing Zheng, Mengfan Duan, Xiaoyun Sun, Guang Han, Qiang Jin   +4 more
openaire   +1 more source

A Probabilistic Fractional Order Physics Informed Mamba Kolmogorov‐Arnold Network for State‐of‐Charge Estimation in Grid‐Connected Battery Energy Storage Systems

Energy Science &Engineering, EarlyView.
This paper introduces the probabilistic fractional‐order Mam‐KAN (PFO‐Mam‐KAN) controller, a physics‐informed gray‐box framework for real‐time battery state‐of‐charge estimation. By unifying efficient Mamba encoders with uncertainty‐aware fractional physics, it achieves superior 0.31% RMSE accuracy and robust grid‐support operation under dynamic ...
Arun Kumar Rawat, Subhash Chandra, Vinay Kumar Deolia   +2 more
wiley   +1 more source

Research on explainable BiGRU deep learning framework for short term load forecasting in smart power systems

Discover Artificial Intelligence
This study proposes a novel forecasting framework based on a parallel multi-input Bi-GRU architecture combined with a sliding window-based Multi-Input Multi-Output (MIMO) prediction strategy.
Zhiwei Wang, Mengzhou Xu, Jingchang Hao, Wei Li, Shiyao Cheng   +4 more
doaj   +1 more source