A New BP Neural Network Algorithm and Its Application in English Education Evaluation

Yu, Zhike

doi:10.11591/telkomnika.v11i11.2970

Cited by 3 publications

(3 citation statements)

References 6 publications

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“… Lino, Rocha & Sizo (2019) applied BPNN to an automatic evaluation in a virtual teaching environment, optimized it by Bayesian regularization, and simulated expert scoring through different evaluation models. Zhike (2013) uses the immune genetic algorithm to improve the standard BPNN algorithm. Taking English teaching performance evaluation data from three universities as examples, the effectiveness and feasibility of the improved algorithm are verified.…”

Section: Related Workmentioning

confidence: 99%

Evaluation scheme design of college information construction based on a combined algorithm

Shen

Shi

Fang

2023

PeerJ Computer Science

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By controlling the benefits and drawbacks of informatization construction (IC) and development, evaluating the level of education informatization (EI) development can aid in university administration and decision-making. This work develops an evaluation method for the University Information Construction (UIC) based on the Analytical Hierarchy Process (AHP) and the Particle Swarm Optimization-based back-Propagation Neural Network (PSO-BPNN) algorithm to address the fuzziness issue in grade evaluation in the IC. Firstly, a set of data-driven evaluation index systems of the UIC effect is constructed with 16 second-class indicators and four first-class indicators of infrastructure, resource management, information management, and safeguard measures. The AHP method is used to determine the weight of the first-class indicators of the IC model. Secondly, from two perspectives of inertia weight and learning factor, the PSO-BPNN algorithm is designed to fit and analyze the level of UIC. The experimental findings demonstrate that the proposed model’s training impact is better, reflecting UIC’s effectiveness more accurately.

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Section: Related Workmentioning

confidence: 99%

Evaluation scheme design of college information construction based on a combined algorithm

Shen

Shi

Fang

2023

PeerJ Computer Science

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“…One is the fission of allele values, and the other is the fusion of allele values in order to unite clusters or strengthen clustering. As shown in Figure 2 , the distribution of social platform data uses GA and working on the Hadoop MapReduce framework, where the social platform data is divided equally into mappers and illustrates the initialization, adaptive function, and selection process to submit the data to the reducer according to the fitness function cross pursuit and mutation, where Mi, Ri, and Ci denote the form of transformation at different stages [ 21 ].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Research on College English Classroom Teaching Model Based on Adaptive Genetic Algorithm

Yang

2022

Computational Intelligence and Neuroscience

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While college English teaching is steadily changing from static knowledge transfer to dynamic language ability development, classroom activities centered on language application are becoming more and more important in cultivating students’ language application ability. In recent years, education has been paid more and more attention, the scale of university education has gradually expanded, the professional categories have become more and more complete, the curriculum has become larger and larger, and the number of students has grown by leaps and bounds. The teaching resources (teachers, classrooms, teaching equipment, etc.) and the workload of English teachers are increasing. In order to effectively improve the efficiency of college English teaching, the paper proposes to apply genetic algorithms to the actual English course scheduling problem in colleges, taking into account all the various hardware and software constraints and the expected course scheduling goals, so as to provide a clear and concise solution to the course scheduling problem plan (parallel search for optimal scheduling) and the design and coding structure of each genetic operator. Furthermore, this study creates a genetic algorithm-based English social platform and examines the design aspects of dynamic teaching models and classroom activities of college English students in the context of this paper.

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“…In this case is to carry out identification using neural networks. This research which uses neural networks has started many done like: A New Neural Network Back Propagation Algorithm and Its Application in English Education Evaluation [1]. The paper improves the original Neural Network Back Propagation with immune genetic algorithm to speed up its calculations and presents a new convergence of Neural Network Back Propagation algorithm for evaluating system performance in English education, A Novel Method to Optimize the Structure of BP Neural Networks [2].…”

Section: Introductionmentioning

confidence: 99%

Identification of Lung Cancer Using a Back Propagation Neural Network

Irianto

Mak'ruf

Titisari

2015

TIJEE

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Reading image of lung cancer screening well-known as X-ray by practitioners are sometimes subjective. This research tried to create software that can detect lung cancer as a comparison of the work of medical practitioners using artificial neural networks (ANN), with X-ray movies taken from the tool diagnostic radiography (DR) stored in the compact disc. The dependent variable observation in this study is the identification of DR X-ray image size of 1024 x 1024 pixels. A total of 10 images X-ray which has been observed by the physician radiology. With 5 images X-ray normal and 5 images lung cancer. In this study, the image processing is done through three stages: neighborhood averaging, median filter and histogram equalization. The result of these features are grouped in normal categories. From test results stating the truth 80%. To facilitate the user in the lung disease pattern recognition. GUI applications design using MATLAB. We use some form of image processing which includes form training andtesting. The best parameters obtained from this research include learning rate=0.3, the number of hidden layer=30 and tolerance error=10-8. From the results obtained by the level of accuracy of the training image of normal lung, lung cancer in a row is 80%. Overall the level of accuracy of the results is 80%.

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A New BP Neural Network Algorithm and Its Application in English Education Evaluation

Cited by 3 publications

References 6 publications

Evaluation scheme design of college information construction based on a combined algorithm

Evaluation scheme design of college information construction based on a combined algorithm

Research on College English Classroom Teaching Model Based on Adaptive Genetic Algorithm

Identification of Lung Cancer Using a Back Propagation Neural Network

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