scholarly journals Piano Performance and Music Automatic Notation Algorithm Teaching System Based on Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yaokun Yang
Keyword(s):  
Artificial Intelligence ◽  
Automatic Recording ◽  
Learning System ◽  
Utilization Rate ◽  
Music Notation ◽  
Piano Performance ◽  
Recording Technology ◽  
Teaching System ◽  
Operation Process ◽  
System Data

Artificial intelligence is a subject that studies all kinds of human intelligent activities and their laws. It is developed on the basis of the cohesion of many disciplines such as computer science, politics, information system, neurophysiology, psychology, philosophy, and language. This paper aims to study how to build a computer or intelligent machine, including hardware and software, imitate and expand the human brain to perform thinking functions such as thinking, programming, arithmetic, and learning, solve complex problems that need to be handled by professionals, and better apply the artificial intelligence assistance system to the teaching of piano performance. In this paper, Prolog language and music-assisted learning system based on the ARM and SA algorithm are proposed, the principle and operation process of music automatic recording technology are deeply studied, and the system data of artificial intelligence are summarized and analyzed by using internal database, so as to find out the implementation principle and law of piano automatic recording system. So that the artificial intelligence assistant system can be better applied to music teaching. The experimental results show that, in the teaching system of piano performance and music automatic notation algorithm, the utilization rate of artificial intelligence auxiliary technology has reached 56.81 and is growing rapidly. Therefore, we can find that the artificial intelligence assistant system plays an important role in the teaching system of piano performance and automatic music notation.

scholarly journals Artificial intelligence-based education assists medical students’ interpretation of hip fracture

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Chi-Tung Cheng ◽  
Chih-Chi Chen ◽  
Chih-Yuan Fu ◽  
Chung-Hsien Chaou ◽  
Yu-Tung Wu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Education ◽  
Hip Fracture ◽  
Medical Students ◽  
Medical Image ◽  
Learning System ◽  
Significant Difference ◽  
Dynamic Collaboration ◽  
Increase In Accuracy ◽  
Image Learning

Abstract Background With recent transformations in medical education, the integration of technology to improve medical students’ abilities has become feasible. Artificial intelligence (AI) has impacted several aspects of healthcare. However, few studies have focused on medical education. We performed an AI-assisted education study and confirmed that AI can accelerate trainees’ medical image learning. Materials We developed an AI-based medical image learning system to highlight hip fracture on a plain pelvic film. Thirty medical students were divided into a conventional (CL) group and an AI-assisted learning (AIL) group. In the CL group, the participants received a prelearning test and a postlearning test. In the AIL group, the participants received another test with AI-assisted education before the postlearning test. Then, we analyzed changes in diagnostic accuracy. Results The prelearning performance was comparable in both groups. In the CL group, postlearning accuracy (78.66 ± 14.53) was higher than prelearning accuracy (75.86 ± 11.36) with no significant difference (p = .264). The AIL group showed remarkable improvement. The WithAI score (88.87 ± 5.51) was significantly higher than the prelearning score (75.73 ± 10.58, p < 0.01). Moreover, the postlearning score (84.93 ± 14.53) was better than the prelearning score (p < 0.01). The increase in accuracy was significantly higher in the AIL group than in the CL group. Conclusion The study demonstrated the viability of AI for augmenting medical education. Integrating AI into medical education requires dynamic collaboration from research, clinical, and educational perspectives.

Design of English reading and learning management system in college education based on artificial intelligence

2021 ◽  
pp. 1-10
Author(s):  
Fen Zhang ◽  
Min She
Keyword(s):  
Artificial Intelligence ◽  
Model Performance ◽  
College Education ◽  
Personalized Learning ◽  
Learning System ◽  
Model Function ◽  
English Reading ◽  
Function Modules ◽  
Learning Platforms ◽  
Learning Management

English reading learning in college education is an efficient means of English learning. However, most of the current English reading learning platforms in colleges and universities only put different English books on the platform in electronic form for students to read, which leads to blindness of reading. Based on artificial intelligence algorithms, this paper builds model function modules according to the needs of English reading and learning management in college education and implements system functions based on artificial intelligence algorithms. Moreover, according to the above design principles of personalized learning model and the characteristics of personalized network learning, this paper designs a personalized learning system based on meaningful learning theory. In addition, this article verifies and analyzes the model performance. The research results show that the model proposed in this paper has a certain effect.

Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of Artificial Intelligence: A pilot Study

Endoscopy ◽  
2020 ◽  
Author(s):  
Alanna Ebigbo ◽  
Robert Mendel ◽  
Tobias Rückert ◽  
Laurin Schuster ◽  
Andreas Probst ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Pilot Study ◽  
White Light ◽  
Submucosal Invasion ◽  
Learning System ◽  
Endoscopic Images ◽  
Barrett’S Cancer ◽  
Significant Difference ◽  
Sensitivity Specificity

Background and aims: The accurate differentiation between T1a and T1b Barrett’s cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an Artificial Intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett’s cancer white-light images. Methods: Endoscopic images from three tertiary care centres in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross-validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) was evaluated with the AI-system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett’s cancer. Results: The sensitivity, specificity, F1 and accuracy of the AI-system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.73 and 0.71, respectively. There was no statistically significant difference between the performance of the AI-system and that of human experts with sensitivity, specificity, F1 and accuracy of 0.63, 0.78, 0.67 and 0.70 respectively. Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett’s cancer. AI scored equal to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and in a real-life setting. Nevertheless, the correct prediction of submucosal invasion in Barret´s cancer remains challenging for both experts and AI.

scholarly journals Mobile English Teaching System Based on Adaptive Algorithm

2018 ◽  
Vol 13 (08) ◽  
pp. 64 ◽  
Author(s):  
Xing Zhao
Keyword(s):  
Adaptive Algorithm ◽  
Learning System ◽  
Learning Ability ◽  
English Learning ◽  
Learning Space ◽  
Teaching System ◽  
English Teaching ◽  
Three Layer Architecture ◽  
Hypermedia Model ◽  
Feedback Module

To improve the students’ individualized and autonomous learning ability in English teaching, a mobile English learning system is designed on the basis of adaptive algorithm. The students’ need for the adaptive mobile English teaching system is analyzed through researches on students and questionnaires. According to the needs analysis, the main functional modules of the adaptive mobile English learning system are designed, including the creation module, personalized learning module, evaluation and feedback module, and management module. Then, the improved XAHM (XML adaptive hypermedia model) is applied to the mobile English learning system. The three-layer architecture of the English mobile learning system is revised into four layers of composition layer, data layer, business logic layer and presentation layer. At the same time, more attention is diverted to the terminal and the situation. Finally, the system is tested. The test results showed that the mobile English learning system realized the self-adaptive and intelligent navigation of learning space in the course of teaching. It is concluded that the new adaptive algorithm had a good performance for college English learning.

scholarly journals Can Patient Safety Incident Reports Be Used to Compare Hospital Safety? Results from a Quantitative Analysis of the English National Reporting and Learning System Data

PLoS ONE ◽  
2015 ◽  
Vol 10 (12) ◽  
pp. e0144107 ◽  
Author(s):  
Ann-Marie Howell ◽  
Elaine M. Burns ◽  
George Bouras ◽  
Liam J. Donaldson ◽  
Thanos Athanasiou ◽  
...  
Keyword(s):  
Patient Safety ◽  
Quantitative Analysis ◽  
Learning System ◽  
Hospital Safety ◽  
Safety Incident ◽  
Patient Safety Incident ◽  
System Data ◽  
Incident Reports

scholarly journals The views of female students towards teaching using CCTV and teaching using e-learning at the Faculty of theology of Imam Muhammad bin Saud Islamic University: آراء الطالبات نحو التدريس باستخدام الدائرة التلفزيونية المغلقة والتدريس باستخدام التعليم الإلكتروني بكلية أصول الدين بجامعة الإمام محمد بن سعود الإسلامية

2017 ◽  
Vol 1 (3) ◽  
Author(s):  
Najwa bint Fathi bin Sweid Tamihi
Keyword(s):  
Female Students ◽  
Learning System ◽  
Closed Circuit ◽  
Teaching System ◽  
Closed Circuit Television ◽  
Arithmetic Average ◽  
The Third ◽  
Television Teaching ◽  
E Learning ◽  
Descriptive Method

The aim of this study was to identify the views of female students of the faculty of the fundamentals of religion at the Imam Muhammad bin Saud Islamic University towards teaching using closed circuit television, teaching using e-learning and finding out if there are statistically significant differences between the views of female students due to the variable system used in teaching. The study sample consisted of (519) randomly selected students from all levels of study. They were divided into two groups: 312 female students who took courses through closed circuit television and 207 female students enrolled in the e-learning system. In order to achieve the objectives of the study, the researcher used the descriptive descriptive method and designed a questionnaire consisting of five areas consisting of (35) paragraphs divided into five areas; namely, teaching inputs, teaching processes, assessment processes, teaching outputs, teaching environment. The study found a number of results: The general arithmetic average of the responses of the members of the sample of female students studied using the closed circuit television on the study instrument areas (3.36 of 5). This average is in the third category of the five-dimensional scale indicating neutral. The general arithmetic average of the responses of the members of the developed students enrolled in the e-learning system was (3.56 out of 5). This average is in the fourth category of the five-step scale indicating the degree of (OK). There are statistically significant differences at the level of (α = 0.05) between the views of the two categories of female students, depending on the variable teaching system between the teaching class using CCTV and the teaching class using e-learning for e-learning students. In the light of the results of the study, a series of recommendations were made to improve teaching using closed-circuit television and teaching using e-learning.

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