Mineral Prospectivity Mapping via Gated Recurrent Unit Model

Yin, Bojun; Xiong, Yihui

doi:10.1007/s11053-021-09979-2

Cited by 32 publications

(2 citation statements)

References 71 publications

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“…However, a variant known as the GRU has fewer parameters and demonstrates speedier convergence performance. The BiGRU network can learn the correlation between present load and factors that affect previous and future loads, which is more beneficial for obtaining the deep characteristics of load data [29].…”

Section: Proposed Methodsmentioning

confidence: 99%

Discharge Summaries Based Sentiment Detection Using Multi-Head Attention and CNN-BiGRU

Waheeb¹

2023

Computer Systems Science and Engineering

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Automatic extraction of the patient's health information from the unstructured data concerning the discharge summary remains challenging. Discharge summary related documents contain various aspects of the patient health condition to examine the quality of treatment and thereby help improve decision-making in the medical field. Using a sentiment dictionary and feature engineering, the researchers primarily mine semantic text features. However, choosing and designing features requires a lot of manpower. The proposed approach is an unsupervised deep learning model that learns a set of clusters embedded in the latent space. A composite model including Active Learning (AL), Convolutional Neural Network (CNN), BiGRU, and Multi-Attention, called ACBMA in this research, is designed to measure the quality of treatment based on discharge summaries text sentiment detection. CNN is utilized for extracting the set of local features of text vectors. Then BiGRU network was utilized to extract the text's global features to solve the issues that a single CNN cannot obtain global semantic information and the traditional Recurrent Neural Network (RNN) gradient disappearance. Experiments prove that the ACBMA method can demonstrate the effectiveness of the suggested method, achieve comparable results to state-of-arts methods in sentiment detection, and outperform them with accurate benchmarks. Finally, several algorithm studies ultimately determined that the ACBMA method is more precise for discharge summaries sentiment analysis.

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Section: Proposed Methodsmentioning

confidence: 99%

Discharge Summaries Based Sentiment Detection Using Multi-Head Attention and CNN-BiGRU

Waheeb¹

2023

Computer Systems Science and Engineering

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“…In hidden layers, GRU can balance performance and size well. In addition, the addition of sparse attention mechanism can improve the problems caused by long-distance transmission in GRU layer [14][15]. To verify the effectiveness of Bert BiGRU depth learning algorithm, corresponding control experiments were conducted, and the specific process is shown in Figure 3.…”

Section: Study On Bert Bigru Deep Learning Algorithmmentioning

confidence: 99%

Research on emotion analysis of e-commerce product reviews based on deep learning

Duan¹,

Jiang²,

Erlin³

2023

Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022)

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The rapid development of e-commerce has prompted the emergence of massive product reviews. Quickly grasping the emotional tendencies in e-commerce product reviews has become an effective way for businesses to improve their problems. Therefore, based on Bert and GUP, the research proposes Bert BiGRU deep learning algorithm, and verifies its effectiveness in the emotional analysis of e-commerce product reviews. The experimental results show that Bert BiGRU algorithm has the highest accuracy of 95.51% among the seven algorithms. In addition, Bert BiGRU algorithm has achieved good training effect after only eight times of training. In addition, Bert BiGRU algorithm has been used in practice to analyze the emotional tendencies in four mobile phone reviews, which shows high reliability. In a word, Bert BiGRU algorithm has high performance and low cost. It has high practicability in the emotional analysis of e-commerce product reviews.

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Graph Deep Learning Model for Mapping Mineral Prospectivity

2022

Math Geosci

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Mineral Prospectivity Mapping via Gated Recurrent Unit Model

Cited by 32 publications

References 71 publications

Discharge Summaries Based Sentiment Detection Using Multi-Head Attention and CNN-BiGRU

Discharge Summaries Based Sentiment Detection Using Multi-Head Attention and CNN-BiGRU

Research on emotion analysis of e-commerce product reviews based on deep learning

Graph Deep Learning Model for Mapping Mineral Prospectivity

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