Education quality detection method based on the probabilistic neural network algorithm

Wu, Chen; Jiang, Hua; Wang, Ping; Deng, Zhenli

doi:10.29354/diag/127194

Cited by 5 publications

(3 citation statements)

References 31 publications

(34 reference statements)

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“…Larson et al proposed the application of the "survival of the fittest" principle to maintain the balance of the resource pool, establish a convenient feedback mechanism, promote the coevolution of the resource pool, promote the flow of information in the resource pool, and improve the utilization rate of resources [4]. From the perspective of basic knowledge of computer networks and ecology, Wu studied the network ecosystem, analyzed the structure, characteristics, and attributes of the ecosystem [5], and put forward some new concepts for a network system. rough the study of some network behavior of the network (including the competition between different network technologies or software), based on the model of population ecology, the competition model between network populations was constructed, and the software was used to simulate it, so as to provide a reference for the optimization and allocation of network resources.…”

Section: Literature Reviewmentioning

confidence: 99%

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

Tao

2022

Mathematical Problems in Engineering

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The key sentimental words in the text cannot be paid attention to effectively, and language knowledge such as the text information and the sentimental resources are relied on. Therefore, it is necessary to make full use of this unique sentimental information to achieve the best performance of the model. In order to solve the problems, a method based on the fusion of the convolutional neural network and the bidirectional GRU network text sentiment analysis capsule model to analyze the ideological and political education of public opinion is put forward. In this model, each sentiment category is combined with the attention mechanism to generate feature vectors to construct sentiment capsules. Finally, the text sentiment categories are judged according to the attributes of the capsules. The model is tested on MR, IMDB, SST-5, and the data set of the ideological and political education review. Experimental results show that compared with MC-CNN-LSTM, the readiness rate of the proposed model is improved by 5.1%, 2.8%, 2.8%, and 1.6% on four public Chinese and English data sets, respectively. Compared with LR-Bi-LSTM, NSCL, and multi-Bi-LSTM models, the accuracy of the proposed MC-BiGRU-Capsule model on MR and SST-5 data are 3.2%, 2.4%, and 3.4% higher than that of the LR-Bi-LSTM, NSCL, and multi-Bi-LSTM models, respectively. It also shows a better classification effect on multiclassification data sets. It is concluded that compared with other baseline models, this method has a better classification effect.

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Section: Literature Reviewmentioning

confidence: 99%

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

Tao

2022

Mathematical Problems in Engineering

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“…ese game data will be completely recorded and divided into three categories: attack data, defense data, and pass data. Important data events will also be scored based on 6 points and given different weights [6,7]. e data that have a direct impact on the score, such as goals, assists, and shots, naturally obtain a higher weight.…”

Section: Introductionmentioning

confidence: 99%

Football Match Scoring Method Based on Adaptive Neural Network Algorithm

Yun

2022

Security and Communication Networks

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To study in more detail the impact of various indicators on the scoring system of a football match, the author suggests a football game scoring method based on an adaptive neural network algorithm. Firstly, the application background of football match prediction, the research and application status of the adaptive neural network algorithm, and the related research of football match prediction are described; Secondly, the factors affecting the outcome of football games are analyzed, and the applicability of the adaptive neural network algorithm in football match prediction are summarized. Through the system requirement analysis, the overall design of the system and the design of the database are completed. From here, we see that there is a huge opportunity to use an adaptive neural network in an automatic scoring system after a football match, which can replace manual scoring and reduce the manual workload.

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“…Untuk menunjang hasil estimasi digunakan algoritma Probabilistic Neural Network (PNN). Kelebihan yang didapatkan dari penerapan PNN yaitu pelatihan yang bersifat mudah, bobot tidak terlatih tetapi ditugaskan sehingga tidak akan berganti dan hanya vektor baru yang dimasukkan ke dalam matriks bobot ketika pelatihan [7]. Penelitian dengan studi kasus prediksi kelulusan tepat waktu Mahasiswa menggunakan metode Decision Tree dan Artificial Neural Network (ANN) menghasilkan tingkat akurasi tinggi sebesar 74,51% untuk metode Decision Tree dan akurasi sebesar 79,74% dihasilkan oleh metode ANN [8].…”

Section: Pendahuluanunclassified

Dimensional Data Unsupervised Learning Using an Analytic Hierarchy Process in Determining Attributes in the Classification Algorithm

Putri¹,

Mustakim²

2022

bits

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Systematic keyword is needed in improving the quality of higher education, one of which is the needed to increase the competence of graduates every year. In increasing student graduation, it is necessary to classify student graduation to find out whether the student is said to be on time (TW) or possibility on time (KTW) using the BPNN and PNN methods. The data used is the Alumni data of the 2013-2020 Information System study program with 7 criteria use, namely GPA, Total Credits, Number of Repetitive Courses, Taking TA Curse in Semester 7, Procrastination, Self-Confidence, and Discipline. The data obtained is then carried out in the process of sharing training data and testing data using K-Means Clustering with the aim; of getting the best accuracy results. Furthermore, the classification stage using BPNN and PNN resulted in an accuracy of 98% and 95% with learning rate of 0.125 and a spread value of 0.1

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Education quality detection method based on the probabilistic neural network algorithm

Cited by 5 publications

References 31 publications

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

Football Match Scoring Method Based on Adaptive Neural Network Algorithm

Dimensional Data Unsupervised Learning Using an Analytic Hierarchy Process in Determining Attributes in the Classification Algorithm

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