scholarly journals Research on the Model of Music Sight-Singing Guidance System Based on Artificial Intelligence

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Tong Zhe
Keyword(s):  
Artificial Intelligence ◽  
Teaching Methods ◽  
Learning System ◽  
System Structure ◽  
Guidance System ◽  
Independent Learning ◽  
Sight Singing ◽  
Recommendation Algorithm ◽  
Learner Model ◽  
Scoring Algorithm

Intelligent Guided Learning System (ITS) is a computer system that uses computers to imitate the experience and methods of teaching experts to assist in teaching; ITS provides learners with personalized learning resources and adaptive teaching methods, thus reducing students’ dependence on teachers and realizing independent learning. After years of research and development, and with the help of artificial intelligence technology, ITS has basically formed a stable structure and unified implementation specifications and has given birth to many excellent products in disciplines such as basic computer, language, medicine, and mathematics. Based on the above background, this paper takes the system structure of universal ITS as the basis, combines the characteristics of music sight-singing subject in teaching contents and teaching methods, researches the intelligent guidance system model of music sight-singing, and completes the design of the overall system architecture. The specific strategies and implementation methods of the teaching methods, resource recommendation, and ability assessment components of the teaching model are studied. The research includes the definition of the difficulty characteristics of the score, the design of the score recommendation algorithm, the design of the sight-singing scoring algorithm, and the experimental analysis of each algorithm. It is proposed that the difficulty feature-based score recommendation algorithm is the core component of resource recommendation in the teacher model, and the sight-singing scoring algorithm is the basis for ability assessment and learner model update.

An artificial intelligence fuzzy system for improvement of physical education teaching method

2020 ◽  
pp. 1-10
Author(s):  
Gaobin ◽  
Cao Huan Nan ◽  
Liu Zhen Zhong
Keyword(s):  
Artificial Intelligence ◽  
Physical Education ◽  
Teaching Methods ◽  
Evaluation System ◽  
Teaching Method ◽  
Motion Vector ◽  
System Structure ◽  
Functional Requirements ◽  
Experimental Teaching ◽  
Vector Decomposition

There are certain disadvantages in the traditional physical education teaching model. In order to improve the advanced nature of physical education teaching methods, this paper builds a physical education evaluation system based on artificial intelligence fuzzy algorithm. The system uses fuzzy control instructions as the basis to combine human language and mechanical language, so that the machine can recognize human working language habits and execute commands according to the instructions. Moreover, in this study, the trapezoid function is selected as the membership function, and the improved particle optimization algorithm is used to capture the student’s motion process and the motion vector decomposition, and the system structure model is constructed based on the functional requirements analysis. In addition, this study conducts system performance analysis through experimental teaching methods. The research results show that this system can effectively promote the reform of teaching methods in physical education and has a certain practical effect.

scholarly journals The Teaching Pattern of Law Majors Using Artificial Intelligence and Deep Neural Network Under Educational Psychology

2021 ◽  
Vol 12 ◽  
Author(s):  
Di Xuan ◽  
Delong Zhu ◽  
Wenhai Xu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Educational Psychology ◽  
Deep Neural Network ◽  
Teaching Quality ◽  
Personalized Learning ◽  
Learning System ◽  
Learning Material ◽  
Recommendation Algorithm ◽  
Advantages And Disadvantages

With the increasing attention to the cultivation of legal talents, a new teaching model has been explored through artificial intelligence (AI) technology under educational psychology, which focuses on improving learning initiative, teaching methods, and teaching quality of students. First, the application of AI and deep neural network (DNN) algorithms are reviewed in education, and the advantages and disadvantages of traditional learning material recommendation algorithms are summarized. Then, a personalized learning material recommendation algorithm is put forward based on DNN, together with an adaptive learning system based on DNN. Finally, the traditional user-based collaborative filtering (UserCF) model and lifelong topic modeling (LTM) algorithm are introduced as the control group to verify the performance of the proposed recommendation system. The results show that the best learning rate of model training is 0.0001, the best dropout value is 0.5, and the best batch size is 32. The proposed personalized learning resource recommendation method based on deep learning (DL) still has good stability under various training data scales. The personalized test questions of recommended students are moderately difficult. It is easier to recommend materials according to the acquisition of knowledge points and the practicability of the recommended test questions of students. Personalized learning material recommendation algorithm based on AI can timely feedback needs of students, thereby improving the effect of classroom teaching. Using the combination of AI and DL algorithms in teaching design, students can complete targeted personalized learning assignments, which is of great significance to cultivate high-level legal professionals.

Top 100 most-cited articles on medical imaging applications of artificial intelligence: a bibliometric analysis (Preprint)

2020 ◽  
Author(s):  
Ying Liu ◽  
Ziyan Yu ◽  
Shuolan Jing ◽  
Honghu Jiang ◽  
Chunxia Wang
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Bibliometric Analysis ◽  
Clinical Medicine ◽  
Medical Physics ◽  
Medical Image Analysis ◽  
The United States ◽  
Independent Learning ◽  
Learning Ability ◽  
Core Technology

BACKGROUND Artificial intelligence (AI) has penetrated into almost every aspect of our lives and is rapidly changing our way of life. Recently, the new generation of AI taking machine learning and particularly deep convolutional neural network theories as the core technology, has stronger learning ability and independent learning evolution ability, combined with a large amount of learning data, breaks through the bottleneck limit of model accuracy, and makes the model efficient use. OBJECTIVE To identify the 100 most cited papers in artificial intelligence in medical imaging, we performed a comprehensive bibliometric analysis basing on the literature search on Web of Science Core Collection (WoSCC). METHODS The 100 top-cited articles published in “AI, Medical imaging” journals were identified using the Science Citation Index Database. The articles were further reviewed, and basic information was collected, including the number of citations, journals, authors, publication year, and field of study. RESULTS The highly cited articles in AI were cited between 72 and 1,554 times. The majority of them were published in three major journals: IEEE Transactions on Medical Imaging, Medical Image Analysis and Medical Physics. The publication year ranged from 2002 to 2019, with 66% published in a three-year period (2016 to 2018). Publications from the United States (56%) were the most heavily cited, followed by those from China (15%) and Netherlands (10%). Radboud University Nijmegen from Netherlands, Harvard Medical School in USA, and The Chinese University of Hong Kong in China produced the highest number of publications (n=6). Computer science (42%), clinical medicine (35%), and engineering (8%) were the most common fields of study. CONCLUSIONS Citation analysis in the field of artificial intelligence in medical imaging reveals interesting information about the topics and trends negotiated by researchers and elucidates which characteristics are required for a paper to attain a “classic” status. Clinical science articles published in highimpact specialized journals are most likely to be cited in the field of artificial intelligence in medical imaging.

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 Development and optimization of a portable guidance system for the blind and visually impaired with the application of artificial intelligence

2021 ◽  
Author(s):  
Tamas Nemes
Keyword(s):  
Artificial Intelligence ◽  
Visually Impaired ◽  
Obstacle Detection ◽  
Guidance System ◽  
Performance Standard ◽  
Audio Signals ◽  
Blind And Visually Impaired ◽  
Improve Accuracy ◽  
New Type ◽  
Map Accuracy

This work describes a new type of portable, self-regulating guidance system, which learns to recognize obstacles with the help of a camera, artificial intelligence, and various sensors and thus warn the wearer through audio signals. For obstacle detection, a MobileNetV2 model with an SSD attachment is used which was trained on a custom dataset. Moreover, the system uses the data of motion and distance sensors to improve accuracy. Experimental results confirm that the system can operate with 74.9% mAP accuracy and a reaction time of 0.15 seconds, meeting the performance standard for modern object detection applications. It will also be presented how those affected commented on the device and how the system could be transformed into a marketable product.

Impact and Latest Trends of Intelligent Learning With Artificial Intelligence

2021 ◽  
pp. 172-189
Author(s):  
Aditi Sakalle ◽  
Pradeep Tomar ◽  
Harshit Bhardwaj ◽  
Uttam Sharma
Keyword(s):  
Artificial Intelligence ◽  
Education System ◽  
Teaching And Learning ◽  
Learning System ◽  
Test Systems ◽  
Human Skills ◽  
Development Of Competencies ◽  
Administrative Tasks ◽  
Increase Efficiency

Artificial intelligence (AI) has been used mainly on education in some methods that contribute to the development of competencies and test systems. With the continued development of educational AI solutions, it is hoped that AI will help address the need for learning, education, and teaching. AI can enhance performance, personalization, and streamline administrative tasks in order to give teachers time and freedom to learn and adapt—uniquely human skills that would battle on machines. The AI dream of education is one where the best results for students are obtained, based on the best qualities of machinery and teachers. The development of curriculum based on the specific needs of individual students has been a concern for educators for many years, but the AI presents teachers with an unprecedented degree of distinction to handle 30 students in each class. With AI many possibilities can be seen in the teaching and learning system considering interest and understanding of an individual, which will increase efficiency of the education system.

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