Exploratory factor analysis, reliability, and validity of the interventionist knowledge and skills survey

Bahr, Michael W.; Edwin, Mary; Long, Kara A.

doi:10.1002/pits.22636

Cited by 5 publications

(4 citation statements)

References 25 publications

(34 reference statements)

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“…That is to divide all the data into k equal parts and run the experiment k times. In each run, one of them is selected as the test data, the other k − 1 as the test data, and the final test result is the average of the k test results [ 33 ]. Since the dimension of this experiment is not large, 4-fold cross-validation is selected.…”

Section: Experiments Of Machine English-assisted Translation Based On...mentioning

confidence: 99%

Artificial Intelligence-based Machine English-Assisted Translation in the Internet of Things Environment

Zhang

Tang

2022

Computational Intelligence and Neuroscience

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With the development of Internet of Things technology, the things that machines do instead of humans are becoming more and more complicated. Machine translation has developed rapidly in the past few decades, and the translation system has also been greatly improved. People’s life and work are inseparable from machine translation, which brings a lot of convenience to people. But machine translation also has many flaws. Although machine translation can translate long texts in a very short time, its translation quality is quite poor, especially in the face of advanced English such as professional English, terminology, abbreviations, etc. To this end, machine English-assisted translation systems have been developed in recent years. Different from the working principle of machine English translation, machine English-assisted translation is a method of artificial intelligence + human-computer interaction. It uses convolutional neural networks and deep learning to translate words efficiently. The translator puts the original text and the translation into the machine database each time, and the machine can process some English terms, complex sentences, technical English, and other advanced English after continuous learning. Machine English-assisted translation can reduce repeated translations and greatly improve translation quality and translation efficiency. In this paper, the combination of artificial intelligence and machine English-assisted translation is compared with machine English translation, and comparative experiments are carried out by setting different matching degrees. Experiments show that the translation efficiency of machine English-assisted translation is much better than that of machine English translation. As the matching rate increases, the translation efficiency of machine English-assisted translation is higher. When the matching rate is greater than 80%, the translation efficiency is three times that of machine English translation. However, it is slightly insufficient in processing pure, simple statements. It highlights the advantages of machine English-assisted translation in terms of term translation and long complex sentences.

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Section: Experiments Of Machine English-assisted Translation Based On...mentioning

confidence: 99%

Artificial Intelligence-based Machine English-Assisted Translation in the Internet of Things Environment

Zhang

Tang

2022

Computational Intelligence and Neuroscience

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“…Sample Data. In order to be able to better analyze the impact of machine learning-based teaching methods on physical health teaching, this paper compares physical health teaching based on machine learning with traditional physical health teaching, mainly through the form of comparative evaluation indicators [24]. Therefore, the selection of the experimental sample is especially critical to the comparison experiment, and the improper sample selection can easily lead to invalid comparison experiment.…”

Section: Experimental Data Comparing Machine Learning-based Physical ...mentioning

confidence: 99%

Development of Machine Learning-Based Ideas for Teaching Physical Education and Health

Yin

Han

2022

BioMed Research International

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With various social pressures and the lack of knowledge about physical health, students have poor physical education quality and insufficient knowledge acquisition about physical health. Traditional physical health teaching is a process in which the teacher tells the theory of physical health and students passively accept it, which leads to physical health problems such as low learning efficiency of students’ physical health knowledge and low interest in learning physical health knowledge. With the emphasis on physical health teaching and the development of technologies such as machine learning, machine learning is used to analyze the problems of physical health teaching and help students to learn physical health better to improve the efficiency of physical health teaching. The results of this paper show that the machine learning-based physical education and traditional physical education can reduce the injury rate of students’ sports by 7.7% compared with traditional physical education, make students’ interest in physical education and health learning reach 53.3%, and improve the efficiency of physical education and health learning. There is a degree of students’ acquisition of physical health knowledge. The change from traditional physical health teaching ideology to machine learning-based physical education ideology can improve the teaching efficiency of physical health teaching, allow students to acquire more physical health knowledge, and effectively reduce the risk of students’ injuries in sports.

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“…In order to test whether the oral health index is effective for the orthodontic experiment analyzed by the mathematical model and the traditional orthodontic experiment, the experiment will be verified by k-fold cross-validation [23].…”

Section: Validity Analysis Of Samplesmentioning

confidence: 99%

Application of Mathematical Model in Orthodontics

Wang

Jiang

Xue³

et al. 2022

Mobile Information Systems

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With the development of digital information technology and big data technology, the medical industry has also undergone tremendous changes. Traditional medical treatment mainly relies on the technical experience of the attending doctor for treatment, and there is no sophisticated instrument or scienti c analysis system to assist in treatment. With the improvement of people's living standards, people's attention to teeth has increased signi cantly. Traditional orthodontics is based on the subjective judgment of orthodontists and manual treatment. Due to the di erences in the experience of orthodontists, the traditional orthodontic e ect is often very poor. Using digital information and big data technology to carry out quantitative diagnosis and treatment analysis of teeth, 3D modeling, and simulation of prosthesis, personalized treatment of the prosthesis model, and nally applied to orthodontics, digital-based orthodontics make the orthodontic diagnosis and treatment process evidence-based, safer, and more e ective. is article compares orthodontics and traditional oral orthodontics based on the mathematical model, to analyze the comfort of orthodontics, the aesthetics of orthodontics, the matching degree of aligners, and the stability of the environment in the periodontal ligament. It is concluded that the average orthodontic comfort based on the mathematical model is 85.6%, and the average aesthetic degree is 64.0%, which are more than 20% better than traditional orthodontics. It is also superior to traditional orthodontics in terms of the degree of matching of the appliance and the stability of the environment in the periodontal ligament. erefore, the combination of mathematical models and orthodontics can lead to better orthodontic results.

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Exploratory factor analysis, reliability, and validity of the interventionist knowledge and skills survey

Cited by 5 publications

References 25 publications

Artificial Intelligence-based Machine English-Assisted Translation in the Internet of Things Environment

Artificial Intelligence-based Machine English-Assisted Translation in the Internet of Things Environment

Development of Machine Learning-Based Ideas for Teaching Physical Education and Health

Application of Mathematical Model in Orthodontics

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