AdaEcoFusion: Adaptive Ecological Feature Fusion for Enhancing Postgraduate Training Work in the Context of “Ecological Civilization”

In the realm of postgraduate training within the framework of ecological civilization, accurately assessing and enhancing training programs is essential. This paper introduces AdaEcoFusion, a framework aimed at improving postgraduate education quality through hypergraph learning techniques and an ad...

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Main Author: Jue Lu
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
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Online Access:https://ieeexplore.ieee.org/document/10849523/
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author Jue Lu
author_facet Jue Lu
author_sort Jue Lu
collection DOAJ
description In the realm of postgraduate training within the framework of ecological civilization, accurately assessing and enhancing training programs is essential. This paper introduces AdaEcoFusion, a framework aimed at improving postgraduate education quality through hypergraph learning techniques and an adaptive ecological feature fusion model. The model categorizes and evaluates training methods by identifying peer groups of educators and mentors who share similar ecological and educational attributes. The framework addresses challenges such as the variability in training approaches across different environments and the availability of diverse ecological data. To address these challenges, AdaEcoFusion uses a hypergraph-based feature fusion that identifies high-quality ecological and educational features, reflecting each mentor’s teaching style and ecological impact. With a probabilistic model, the framework represents each mentor’s attributes in a latent space, offering a nuanced understanding of their contributions. Then we construct a large-scale graph to map relationships and similarities among educators, identifying dense subgraphs or “circles” of mentors with shared attributes. By mining these mentor circles, AdaEcoFusion enhances postgraduate training quality and provides adaptive fusion of ecological features, optimizing the training process in line with the goals of ecological civilization. Experiments conducted on a dataset of postgraduate mentors from 29 prominent universities demonstrate the model’s effectiveness in improving training programs. This underscores its capability to align postgraduate training with the objectives of ecological civilization, fostering a sustainable and adaptive educational system.
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spelling doaj-art-6a34d330e15c4e8e9204a45f793dcda82025-01-29T00:01:03ZengIEEEIEEE Access2169-35362025-01-0113158721588410.1109/ACCESS.2025.353277410849523AdaEcoFusion: Adaptive Ecological Feature Fusion for Enhancing Postgraduate Training Work in the Context of “Ecological Civilization”Jue Lu0https://orcid.org/0009-0008-3428-9048School of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou, ChinaIn the realm of postgraduate training within the framework of ecological civilization, accurately assessing and enhancing training programs is essential. This paper introduces AdaEcoFusion, a framework aimed at improving postgraduate education quality through hypergraph learning techniques and an adaptive ecological feature fusion model. The model categorizes and evaluates training methods by identifying peer groups of educators and mentors who share similar ecological and educational attributes. The framework addresses challenges such as the variability in training approaches across different environments and the availability of diverse ecological data. To address these challenges, AdaEcoFusion uses a hypergraph-based feature fusion that identifies high-quality ecological and educational features, reflecting each mentor’s teaching style and ecological impact. With a probabilistic model, the framework represents each mentor’s attributes in a latent space, offering a nuanced understanding of their contributions. Then we construct a large-scale graph to map relationships and similarities among educators, identifying dense subgraphs or “circles” of mentors with shared attributes. By mining these mentor circles, AdaEcoFusion enhances postgraduate training quality and provides adaptive fusion of ecological features, optimizing the training process in line with the goals of ecological civilization. Experiments conducted on a dataset of postgraduate mentors from 29 prominent universities demonstrate the model’s effectiveness in improving training programs. This underscores its capability to align postgraduate training with the objectives of ecological civilization, fostering a sustainable and adaptive educational system.https://ieeexplore.ieee.org/document/10849523/Adaptive ecologicalfeature fusionhypergraph learningpostgraduate trainingecological civilizationmentor circles
spellingShingle Jue Lu
AdaEcoFusion: Adaptive Ecological Feature Fusion for Enhancing Postgraduate Training Work in the Context of “Ecological Civilization”
IEEE Access
Adaptive ecological
feature fusion
hypergraph learning
postgraduate training
ecological civilization
mentor circles
title AdaEcoFusion: Adaptive Ecological Feature Fusion for Enhancing Postgraduate Training Work in the Context of “Ecological Civilization”
title_full AdaEcoFusion: Adaptive Ecological Feature Fusion for Enhancing Postgraduate Training Work in the Context of “Ecological Civilization”
title_fullStr AdaEcoFusion: Adaptive Ecological Feature Fusion for Enhancing Postgraduate Training Work in the Context of “Ecological Civilization”
title_full_unstemmed AdaEcoFusion: Adaptive Ecological Feature Fusion for Enhancing Postgraduate Training Work in the Context of “Ecological Civilization”
title_short AdaEcoFusion: Adaptive Ecological Feature Fusion for Enhancing Postgraduate Training Work in the Context of “Ecological Civilization”
title_sort adaecofusion adaptive ecological feature fusion for enhancing postgraduate training work in the context of x201c ecological civilization x201d
topic Adaptive ecological
feature fusion
hypergraph learning
postgraduate training
ecological civilization
mentor circles
url https://ieeexplore.ieee.org/document/10849523/
work_keys_str_mv AT juelu adaecofusionadaptiveecologicalfeaturefusionforenhancingpostgraduatetrainingworkinthecontextofx201cecologicalcivilizationx201d