Application of an Artificial Neural Network to Evaluate the Integration Effect of Quality Education and Skill Education

Chun, Bai; Huang, Wei

doi:10.3991/ijet.v16i07.22121

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…Bai and Huang [21] have developed that the ability of ANNs to handle nonlinear information adaptively allows for the assessment of the integration impact between high-quality education and skill-based education. Prior to the merging of the two models, the issues and incentive systems were the subject of an investigation.…”

Section: Related Workmentioning

confidence: 99%

Talent Cultivation Quality of Software Engineering Majors Based on Deep Learning

Mengzi Zhang

2024

jes

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Online learning, to cultivate talents it is inevitable to encounter some pictures or videos with poor visual quality. Deep-learning algorithms are both data-hungry and expensive to compute. These algorithms work better after being trained on a broad and extensive collection of samples. The current moment deep learning methods must urgently make use of human intellect to address the issue in a way that reduces the most expensive effort computationally. This paper analyzes the current situation of software engineering talent cultivation quality of software engineering to enhance the quality of the education is improved by Hierarchically Gated Recurrent Neural Network (HGRNN). The aim of the work is to foster the development of world-class software engineering talents. Initially, the input data’s are gathered from public dataset train 400 with 400 grey pictures. HGRNN is image de-noising module, as for the smart teaching platform to assist instructors in obtaining teaching photography with high quality and improve teaching quality. The proposed model is implemented in MATLAB/ Simulink platform and the accuracy is compared to various existing approaches such Back Propagation Network (BPN), Artificial Neural Network (ANN) and Decision Tree Algorithm (DTA) our proposed method obtains 98% of accuracy.

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Section: Related Workmentioning

confidence: 99%

Talent Cultivation Quality of Software Engineering Majors Based on Deep Learning

Mengzi Zhang

2024

jes

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“…Artificial neural network (ANN). The artificial neural network is a biologically inspired ML method that attempts to simulate the tasks of neurons in the human brain [30]. Figure 2 presents an ANN sample.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Optimizing Machine Learning Algorithms for Heart Disease Classification and Prediction

El-Ibrahimi,

Terrada,

El Gannour

et al. 2023

Int. J. Onl. Eng.

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According to the World Health Organization (WHO), cardiovascular disease is one of the leading causes of death worldwide. Thus, the prevention of this kind of illness is considered as a huge human health challenge. Additionally, the diagnostic process often involves a combination of clinical examination, laboratory tests, and other diagnostic procedures, which can be complex and time-consuming. However, advances in medical technology and research have led to improved methods for diagnosing heart disease, which can help to improve patient outcomes. Furthermore, Machine Learning (ML) methods have shown promise in helping to improve the diagnosis of heart disease. Each method requires specific parameters to produce good results. In this paper, we propose a diagnosis support system based on optimized Machine Learning algorithms, which is Artificial Neural Network (ANN), Support Vector Machine (SVM), K_Nearest Neighbour (KNN), Naive Bayes (NB), and Decision Tree (DT) to analyze the major cardiovascular risk factors, such as age, gender, high blood pressure, etc. To train and validate the ML models, a medical dataset of 558 patients with atherosclerosis is used. In this work, we achieved a 96.67% as promising accuracy level for the atherosclerosis prediction with ANN.

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“…This will then promote close collaboration between universities and businesses and can also effectively enhance the job skills of students [19]. Bai C & Huang W believe that excellent education is the precondition and foundation for skill training education, and the comprehensive analysis of quality education and skill education through an artificial neural network to analyze its rationality with indicators presents a new direction for the growth of vocational training education [20].…”

Section: Introductionmentioning

confidence: 99%

Research on the Countermeasures for the Construction of High-level Practical Training Bases in Higher Vocational Colleges Based on the Integration of Industry and Education

2023

Applied Mathematics and Nonlinear Sciences

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To quickly establish high-level training bases in higher vocational colleges while integrating industry and education, this article first describes the concepts and layout of the space. Secondly, Using interest-related data, an assessment method of the indicators that demonstrate the efficiency of bringing together industry and schooling is developed, the weights of the indicators are determined, and the evaluation matrix is constructed for a comprehensive evaluation using ANP and gray fuzzy theory, respectively. Three colleges were used as examples to examine the effectiveness of integrating industry and education in creating advanced knowledge bases. The findings show that the greatest weighting of the indicators for how well industry and education are integrated is 0.3135, and the highest governance economic investment in building high-level training supports is 93.26, suggesting that building high-level training bases can enhance how well industry and education are integrated.

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Application of an Artificial Neural Network to Evaluate the Integration Effect of Quality Education and Skill Education

Cited by 7 publications

References 19 publications

Talent Cultivation Quality of Software Engineering Majors Based on Deep Learning

Talent Cultivation Quality of Software Engineering Majors Based on Deep Learning

Optimizing Machine Learning Algorithms for Heart Disease Classification and Prediction

Research on the Countermeasures for the Construction of High-level Practical Training Bases in Higher Vocational Colleges Based on the Integration of Industry and Education

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