Evaluating the innovation and entrepreneurship education in colleges using BP neural network

He, Mengxing; Zhang, Jinliang

doi:10.1007/s00500-023-09079-8

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…They introduced a cutting-edge evaluation framework using backpropagation neural networks, marking a significant step forward in the enhancement of educational quality. Their thorough comparative analyses of assessment techniques showcased the superiority of their model, which can be instrumental in refining evaluation processes within innovative education environments [63]. The MbMix text classification method can support university students engaged in innovation and entrepreneurship by enabling their businesses to comprehend and analyze vast amounts of text data with precision.…”

Section: Innovative Educationmentioning

confidence: 99%

Machine Learning Algorithms for Fostering Innovative Education for University Students

Wang,

You,

2024

Electronics

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Data augmentation with mixup has been proven effective in various machine learning tasks. However, previous methods primarily concentrate on generating previously unseen virtual examples using randomly selected mixed samples, which may overlook the importance of similar spatial distributions. In this work, we extend mixup and propose MbMix, a novel yet simple training approach designed for implementing mixup with memory batch augmentation. MbMix specifically selects the samples to be mixed via memory batch to guarantee that the generated samples have the same spatial distribution as the dataset samples. Conducting extensive experiments, we empirically validate that our method outperforms several mixup methods across a broad spectrum of text classification benchmarks, including sentiment classification, question type classification, and textual entailment. Of note, our proposed method achieves a 5.61% improvement compared to existing approaches on the TREC-fine benchmark. Our approach is versatile, with applications in sentiment analysis, question answering, and fake news detection, offering entrepreneurial teams and students avenues to innovate. It enables simulation and modeling for student ventures, fostering an entrepreneurial campus culture and mindset.

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Section: Innovative Educationmentioning

confidence: 99%

Machine Learning Algorithms for Fostering Innovative Education for University Students

Wang,

You,

2024

Electronics

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Cultivation Path of Innovation and Entrepreneurship Personnel Training Driven by New Quality Productive Forces

Chen,

Zhang

2024

Lecture Notes on Data Engineering and Communications Technologies

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Efficient Fault Detection and Analysis of Power System Distribution Networks by Integrating BP Data Mining

Liu

2024

Int. J. Rel. Qual. Saf. Eng.

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As the basic guarantee for people’s production and life, the safe operation of the power system has an important impact on the development and operation of society. To ensure the safe and stable operation of the power grid, predicting potential faults and taking reasonable preventive measures can effectively avoid the occurrence of power accidents. However, due to the difficulty in ensuring the prediction accuracy of traditional methods, there are issues of protection misoperation and rejection. Therefore, in order to achieve accurate prediction of power grid faults and avoid protection misoperation and rejection issues, a distribution network fault classification prediction model using a combination of three-layer data mining model (TLDM) and adaptive moment estimation (Adam) algorithm/random gradient descent algorithm improved backpropagation neural network (BPNN) is proposed. The implementation results showed that the classification accuracy of artificial fish school [Formula: see text] priori, [Formula: see text]-means clustering convolutional neural network model and TLDM for single-phase grounding faults was 93.2%, 91.5% and 96.6%, respectively. The classification accuracy for two-phase faults was 92.8%, 92.4% and 95.7%, respectively. The classification accuracy for two-phase grounding faults was 93.7%, 91.2% and 96.9%, respectively. The classification accuracy for three-phase faults was 93.3%, 92.1% and 97.1%, respectively. The TLDM had the highest classification accuracy. The average accuracy, average accuracy and average recall of the BPNN improved by the combination of the ADAM algorithm and random gradient descent algorithm were 94.1%, 90.9% and 88%, respectively, which were higher than the BPNN improved by the combination of ADAM algorithm and random gradient descent algorithm. The above results indicate that the proposed distribution network fault classification and prediction model has good performance and can achieve accurate prediction of distribution network faults.

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Evaluating the innovation and entrepreneurship education in colleges using BP neural network

Cited by 5 publications

References 26 publications

Machine Learning Algorithms for Fostering Innovative Education for University Students

Machine Learning Algorithms for Fostering Innovative Education for University Students

Cultivation Path of Innovation and Entrepreneurship Personnel Training Driven by New Quality Productive Forces

Efficient Fault Detection and Analysis of Power System Distribution Networks by Integrating BP Data Mining

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