A framework for optimizing extended belief rule base systems with improved Ball trees

Fu, Yang-Geng; Zhuang, Jinhui; Chen, Yupeng; Guo, Longkun; Wang, Ying‐Ming

doi:10.1016/j.knosys.2020.106484

Cited by 34 publications

(14 citation statements)

References 25 publications

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“…With the advancement of neural networks, it can help automated inspections to identify defects more effectively. Back Propagation Network (BPN) and even Convolutional Neural Networks (CNN), CNN is a major breakthrough (Fu et al, 2020 , 2021a , b ; Liu et al, 2020c ; Zhang et al, 2021a , b ). Almost all networks are based on CNN to improve and develop.…”

Section: Relative Workmentioning

confidence: 99%

The application of artificial intelligence assistant to deep learning in teachers' teaching and students' learning processes

Liu

Chen²,

Yao³

2022

Front. Psychol.

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With the emergence of big data, cloud computing, and other technologies, artificial intelligence (AI) technology has set off a new wave in the field of education. The application of AI technology to deep learning in university teachers' teaching and students' learning processes is an innovative way to promote the quality of teaching and learning. This study proposed the deep learning-based assessment to measure whether students experienced an improvement in terms of their mastery of knowledge, development of abilities, and emotional experiences. It also used comparative analysis of pre-tests and post-tests through online questionnaires to test the results. The impact of technology on teachers' teaching and students' learning processes, identified the problems in the teaching and learning processes in the context of the application of AI technology, and proposed strategies for reforming and optimizing teaching and learning. It recommends the application of software and platforms, such as Waston and Knewton, under the orientation of AI technology to improve efficiency in teaching and learning, optimize course design, and engage students in deep learning. The contribution of this research is that the teaching and learning processes will be enhanced by the use of intelligent and efficient teaching models on the teachers' side and personalized and in-depth learning on the students' side. On the one hand, the findings are helpful for teachers to better grasp the actual conditions of in-class teaching in real time, carry out intelligent lesson preparations, enrich teaching methods, improve teaching efficiency, and achieve personalized and precision teaching. On the other hand, it also provides a space of intelligent support for students with different traits in terms of learning and effectively improves students' innovation ability, ultimately achieving the purpose of “artificial intelligence + education.”

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Section: Relative Workmentioning

confidence: 99%

The application of artificial intelligence assistant to deep learning in teachers' teaching and students' learning processes

Liu

Chen²,

Yao³

2022

Front. Psychol.

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“…Its purpose is to predict the acceptability of the tool and determine what changes must be made to the system to make it acceptable to users. This model shows that the acceptability of an information system is determined by two main factors: perceived usefulness and ease of use ( Fu et al, 2020 , 2021a , 2021b ). In TAM 3, these aspects are determined by four different types of factors, including individual differences, system characteristics, convenience conditions and community influence.…”

Section: Literature Review and Hypothesesmentioning

confidence: 99%

Research on the state of blended learning among college students – A mixed-method approach

Deng¹,

Jiao²,

2022

Front. Psychol.

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In the wake of the COVID-19 pandemic in 2019, China’s education leaders began to focus on and promote blended learning. The process is still in its infancy in Chinese colleges and universities, and its development remains a problem to be solved. By combining technology acceptance and student participation, this article proposes an analysis model for assessing the factors influencing blended learning. A questionnaire was designed and distributed, and 796 valid responses were collected. The mean and variance were used to examine the status of students’ technology acceptance and satisfaction with blended learning. The t-test method was employed to analyze the gender differences between students in regard to the topic. The results show that: (1) students majoring in computer science view the factors as having a high level of influence in blended learning. (2) There are major variances regarding the perception of service quality between male and female computer science major students. There is no significant difference between them in terms of perceived usefulness, perceived ease of use, or computer self-efficacy. (3) There are considerable disparities in the skill involvement and participation of computer science major college students. The results show that the technology acceptance and participation of students determine the effect of blended learning. Based on these findings, this article provides theoretical and practical suggestions for the implementation of blended learning to improve its effect.

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“…Through literature review, it is found that most authors consider passive IRS to improve the PLS of the system. As in the literature [23][24][25][26][27][28][30][31][32][33][34][35][36][37], the security performance of a passive IRS-assisted system is considered, and the security capacity is maximized by optimizing the reflection phase shift and/or transmission probability of the passive IRS.…”

Section: Introductionmentioning

confidence: 99%

Security performance analysis of active intelligent reflective surface assisted wireless communication

Liang

Xie

et al. 2023

COMSIS J

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As a new communication technology, Intelligent Reflecting Surface (IRS) can intelligently reconfigure the wireless propagation environment by integrating many passive/active reflective elements on the plane. According to the characteristics that IRS can adjust the propagation channel intelligently, this paper applies IRS to wireless security communication, and studies how to make the security rate reach the optimal security capacity from the perspective of optimization technology. In this paper, two schemes of passive/active IRS are considered, and the corresponding safety rate maximization algorithm is proposed. In view of the non convexity of the objective function, on the one hand, in the passive IRS scheme, the Dinkelbach method is used to transform the objective function into a form that is easy to handle, and the original problem is transformed into a convex problem through the continuous convex approximation method; On the other hand, under the active IRS scheme, aiming at the complexity of the original problem, the first order Taylor expansion is used to obtain the lower bound of the optimization problem, and a minimax optimization algorithm is proposed. Finally, the performance of the proposed algorithm is verified by simulation. The simulation results show that the algorithm designed with active IRS has better security rate than the algorithm designed with passive IRS under the same parameter settings.

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A framework for optimizing extended belief rule base systems with improved Ball trees

Cited by 34 publications

References 25 publications

The application of artificial intelligence assistant to deep learning in teachers' teaching and students' learning processes

The application of artificial intelligence assistant to deep learning in teachers' teaching and students' learning processes

Research on the state of blended learning among college students – A mixed-method approach

Security performance analysis of active intelligent reflective surface assisted wireless communication

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