An innovative evaluation method for undergraduate education: an approach based on <i>BP</i> neural network and stress testing

Chang, Liu; Feng, Yan; Wang, Yuling

doi:10.1080/03075079.2020.1739013

Cited by 34 publications

(18 citation statements)

References 31 publications

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“…Radial basis function (RBF) neural network models optimize multisensor fusion techniques. Therefore, the designed model is RS-BP (radial sensor backpropagation) multisensor data fusion technology [24]. Among them, RBF neural network technology is the main technology of this model.…”

Section: Multisensor Controllable Technology Incorporatingmentioning

confidence: 99%

The Influence of Multisensor Fusion Machine Learning on the Controllable Fabrication of MOF (UIO-66)/ZrAl Ceramic Composite Membranes

Shao

et al. 2022

Journal of Sensors

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This study is aimed at improving the utilization efficiency of resources and enhancing the experiments’ effect of various composite membrane research. Firstly, the meaning and preparation process of Metal-Organic Frameworks (MOFs) are discussed. Then, the theoretical knowledge of fusing machine learning and multisensor technology is outlined. Finally, based on the controllable fabrication concept of MOF [UIO- (Universitetet I Oslo-) 66]/ZrAl ceramic composite membranes, a multisensor model incorporating machine learning is designed. The results show that the designed radial sensor backpropagation (RS-BP) fusion multisensor model has the highest error rate of about 0.87. When the number of training is about 100 times, the model’s error rate tends to be stable, and the minimum error rate is about 0.01. Secondly, the maximum adsorption capacity of the composite membrane under the controllable preparation of the model is 800 cm3/g Spanning Tree Protocol (STP). Additionally, the adsorption capacity decreases slowly, and the overall adsorption energy is higher than that of the traditional preparation method. Finally, the catalytic efficiency of membranes prepared by fusing multiple sensors is 90%-97%. The research achieves innovation in technology and improves the feasibility of rational application of MOF (UIO-66)/ZrAl ceramic composite membranes. This study not only provides technical support for the development of machine learning fusion multisensing technology but also contributes to the comprehensive improvement of the resource utilization effect.

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Section: Multisensor Controllable Technology Incorporatingmentioning

confidence: 99%

The Influence of Multisensor Fusion Machine Learning on the Controllable Fabrication of MOF (UIO-66)/ZrAl Ceramic Composite Membranes

Shao

et al. 2022

Journal of Sensors

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“…Then, for the improved BP neural network, choose the number of layers or adjust the number of neurons in the hidden layer. Then, choose the corresponding hidden layer nodes, that is, begin to invest in a few hidden layer nodes [ 13 , 14 ]. Then gradually increase the number of invested nodes until the number of nodes is more manageable.…”

Section: Methodsmentioning

confidence: 99%

Construction of a Prediction Model for Distance Education Quality Assessment Based on Convolutional Neural Network

Wang

2022

Computational Intelligence and Neuroscience

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This paper introduces the principles and operation steps of convolution and pooling of convolutional neural networks in detail. In view of the shortcomings of fixed sampling points and single receptive field in traditional convolution and pooling forms, deformable convolution and deformable pooling are introduced to enhance the network’s ability to adapt to image details and large displacement problems. The concepts of warp, loop optimization, and network stack are introduced. In order to improve the optimization performance of the algorithm, three subnetwork structures and stack models are designed, and various methods are used to improve the prediction accuracy of distance education quality assessment. In order to improve the accuracy and timeliness of education quality assessment, this paper proposes a distance education quality assessment model based on mining algorithms. The prediction index is selected by the improved BP neural network. It is required to establish the input layer node as the input vector based on the number of data sources since the input layer is used for data input. The neural network is trained with a quarter of the mining data, and the mining algorithm is further trained with network error trials. A fuzzy relationship matrix is created based on the assessment of teaching quality’s hierarchical structure. This leads to the conclusion of the fuzzy thorough evaluation of the effectiveness of distant learning. Experiments show that the proposed model has an average accuracy of 96%, the average teaching quality modeling time is 25.44 ms, and the evaluation speed is fast.

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“…In order to measure the quality of college physical education, Feng (2021) has built a teaching quality index system with big data technology and gives artificial intelligent quality data to construct a teaching quality assessment model [14]. e basic characteristics of teaching attitude, teaching content, teaching methods, and teachers, build a postgraduate teaching quality assessment index system and use BP neural network algorithm to build evaluation models, application sensitivity test identification key indicators [15]. Wei et al has built a graduate nursing professional degree education quality assessment index system from the four aspects of input quality, process quality, output quality, and development quality, and empowering weights through Delphi law and level analysis [16].…”

Section: Introductionmentioning

confidence: 99%

Application of BP Neural Network in Teaching Quality Evaluation of Higher Vocational Education

Feng

2022

Mobile Information Systems

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Higher vocational education has developed rapidly and attracted wide attention. Teaching quality evaluation is an important part of teaching management in classroom teaching and is an important part of teaching management. Based on the theory of teaching quality, 16 evaluation indicators are identified from the four aspects of teaching attitude, teaching content, teaching process, and teaching results and an HVE quality evaluation index system is constructed. The evaluation model is established by BP neural network algorithm. Finally, five higher vocational colleges in Henan Province are selected for empirical research. The results show that the evaluation model constructed in this paper has certain applicability and can provide a reference for HVE quality evaluation and improvement. Although the evaluation models herein have certain applicable values, further improvements are still needed in terms of indicator identification and correction.

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An innovative evaluation method for undergraduate education: an approach based on BP neural network and stress testing

Cited by 34 publications

References 31 publications

The Influence of Multisensor Fusion Machine Learning on the Controllable Fabrication of MOF (UIO-66)/ZrAl Ceramic Composite Membranes

The Influence of Multisensor Fusion Machine Learning on the Controllable Fabrication of MOF (UIO-66)/ZrAl Ceramic Composite Membranes

Construction of a Prediction Model for Distance Education Quality Assessment Based on Convolutional Neural Network

Application of BP Neural Network in Teaching Quality Evaluation of Higher Vocational Education

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