Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Construction of an Artificial Intelligence Writing Model for English Based on Fusion Neural Network Model.

Computational intelligence and neuroscience·2022

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 20, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Application of Adaptive Neural Network Algorithm Model in English Text Analysis.

Maosheng Hung¹, Meijin Hsiao²

¹School of Foreign Languages, Fuzhou University of International Studies and Trade, Fuzhou, Fujian 350202, China.

Computational Intelligence and Neuroscience

|June 6, 2022

Summary

This study introduces an adaptive mechanism for neural networks, improving learning rate adaptation and reducing hyperparameter tuning. The enhanced model accurately analyzes English text coherence, boosting performance in composition correction systems.

More Related Videos

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Related Experiment Videos

Last Updated: Sep 20, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Area of Science:

Natural Language Processing
Machine Learning
Computational Linguistics

Background:

Existing optimization neural network algorithms require efficient adaptive mechanisms.
Hyperparameter configuration in deep learning models can be complex and time-consuming.
Accurate analysis of English text coherence is crucial for automated writing systems.

Purpose of the Study:

To introduce a computationally efficient adaptive mechanism (adaptive exponential decay rate) for neural network optimization.
To develop a dual-encoder model integrating Transformer and Bi-GRU for enhanced sentence feature extraction.
To propose an improved subgraph matching algorithm for quantitative analysis of English text coherence.

Main Methods:

Implemented an adaptive exponential decay rate mechanism integrated with the Adadelta algorithm (AEDR-Adadelta).
Utilized a dual-encoder architecture (Transformer encoder for context, Bi-GRU encoder for source sentence information) with a gated decoder.
Employed an improved subgraph matching algorithm on sentence semantic graphs to identify and analyze coherence patterns.

Main Results:

The AEDR-Adadelta algorithm dynamically and adaptively acquires learning rates, reducing hyperparameter configuration complexity.
The proposed model demonstrated improved feature extraction and analysis capabilities through attention mechanisms.
Experimental results show reduced training and test errors, improved classification accuracy, faster convergence, and better text generalization.

Conclusions:

The adaptive mechanism significantly enhances neural network optimization and text analysis.
The semantic coherence diagnosis model effectively captures coherence features in English texts.
The developed model shows promise for improving English composition correction systems and aiding educators.