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Showing 7 of 7 academic articles

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

Wei Liu, Zihui Lin, Yuan Hu, Aodong Tian et al. • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2024)

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 po...

DOAJ Open Access (DOAJ) DOI: 10.1109/JSTARS.2024.3470222

Hyperbolic Neural Networks

Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann • arXiv (Cornell University) (2018)

Hyperbolic spaces have recently gained momentum in the context of machine learning due to their high capacity and tree-likeliness properties. However, the representational power of hyperbolic geometry is not yet on par with Euclidean geomet...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1805.09112

Research on Personalized Recommendation Algorithm Integrating Cross-Grained Sentiment and Rating Interaction Features

Bing Li, Jiangtao Dong, Xile Wang, Yuqi Hou et al. • IEEE Access (2025)

To investigate the impact of cross-grained sentiments on user feature representation and address the issue of data sparsity, this paper proposes a Personalized Recommendation Algorithm Integrating Cross-Grained Sentiment and Rating Interact...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2025.3530431

Attention Pooling-Based Bidirectional GRU Model for Sentimental Classification

Dejun Zhang, Mingbo Hong, Lu Zou, Fei Han et al. • International Journal of Computational Intelligence Systems

Recurrent neural network (RNN) is one of the most popular architectures for addressing variable sequence text, and it shows outstanding results in many natural language processing (NLP) tasks and remarkable performance in capturing long-ter...

DOAJ Open Access (DOAJ) DOI: 10.2991/ijcis.d.190710.001

A State-of-the-Art Survey on Deep Learning Theory and Architectures

Md Zahangir Alom, Tarek M. Taha, Chris Yakopcic, Stefan K.E. Westberg et al. • Electronics (2019)

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some ne...

OPENALEX cc-by DOI: https://doi.org/10.3390/electronics8030292

Rethinking Gated Recurrent Units for Rotating Machinery Prognostics: A Physics-Consistency Benchmark on the Mismatch Between Gating Mechanisms and Degradation Dynamics

Zhonghua Feng, Minglun Ren • Applied Sciences (2026)

Rotating machinery prognostics is essential for ensuring the reliability and operational safety of industrial systems. Although gated recurrent units (GRUs) have achieved competitive performance in remaining useful life (RUL) prediction, wh...

DOAJ Open Access (DOAJ) DOI: 10.3390/app16178379

Hybrid Model Based on an SD Selection, CEEMDAN, and Deep Learning for Short-Term Load Forecasting of an Electric Vehicle Fleet

Ahmad Mohsenimanesh, Evgueniy Entchev, Filip Bosnjak • Applied Sciences (2022)

Forecasting the aggregate charging load of a fleet of electric vehicles (EVs) plays an important role in the energy management of the future power system. Therefore, accurate charging load forecasting is necessary for reliable and efficient...

DOAJ Open Access (DOAJ) DOI: 10.3390/app12189288