Artificial intelligence in ophthalmology: Optimization of machine learning for ophthalmic care and research

Multimodal is one of the methods of teaching or ideas formed from a different set of models and teaching content to enhance the student’s experience in their field of study. This method can be implemented in various scenarios, such as video lessons, interactive elements, and a list of questions. Teachers should have enough ideas to cover students in all the categories. The students have to listen to the courses through an online learning platform or a live session. This proposed work focuses on multimodal teaching and learning concepts combined with wireless sensor networks and AI. Additionally, the implementation of technology in classroom education is being done to handle the multimodal data. This idea is implemented with the Random Offloading Algorithm which works based on the multiagent system. This multiagent system is implemented for optimizing the user’s response delay and hence balancing the effective resource allocation of the server. The main role of this Random Offloading Algorithm in the proposed system is to focus on the online classes for the students through wireless network and to make effective response to the student’s queries. The proposed system is later compared with the existing ANN classification model. The results show that the proposed algorithm has provided 98.99% accuracy in providing better classroom education.

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Classroom Education Model of Artificial Intelligence and Information Technology Supported by Wireless Networks

Yang

Huang

Wang

et al. 2022

“…Its research fields include image processing, pattern recognition and other computer vision fields, speech synthesis and recognition of language communication and understanding, the social common sense in the cognitive field, mechanical control, design and manufacture, the cooperation between robots and humans; and machine learning (ML). Thus, the application field of AI is very broad, covering almost all aspects of life [15,16]. However, the definition of artificial intelligence is not very definite at present.…”

Section: Methodsmentioning

confidence: 99%

Application of Artificial Intelligence and Wireless Networks to Music Teaching

Guan

Ren

2021

The study is aimed at improving student’s learning ability and constructing an intelligent, all-around, and three-dimensional innovative classroom by using intelligent technology. Middle school C is taken as the research object. First, the current situation of music teaching in middle school C is analyzed, and an intelligent music teaching mode suitable for middle school C is designed. Second, the intelligent music teaching mode is applied in the actual teaching process, and students’ feelings for intelligent music classrooms are explored by a questionnaire survey. Finally, the teaching effect is analyzed. The results show that 96.7% of the students hold a positive attitude towards the new teaching mode, indicating that contemporary students have relatively high adaptation to and recognition with the development of artificial intelligence (AI). 84% of the students are not particularly skilled in the operation of the intelligent network platform, and 9.3% of the students believe that the operation of the platform is relatively difficult. Therefore, teachers should teach students how to use tablets. 95% of the students hold that teachers can quickly find teaching resources related to the course content on the tablet and introduce them to students in the teaching process, indicating that teachers are familiar with the usage of new teaching equipment. On the whole, students have a good experience of the new teaching mode. This new teaching mode based on AI is a response to the new course concept proposed in China.

“…Engine service module consists of data mining group and user model. The reason it is called "engine service" is that this module is at the heart of the whole system [17]. Its final result will produce a related user model, which contains various data of the user: age; gender; travel time; travel footprint; travel mode of transportation; distance between tourist attractions and residence.…”

Section: Engine Service Modulementioning

confidence: 99%

Tourist Attraction Recommendation Method Based on Megadata and Artificial Intelligence Algorithm

Yun¹,

Jiao

2022

As China’s economy continues to grow of informational technology and mobile Internet industry, the online tourism industry has received more and more extensive attention and use. However, as an emerging industry, users often need to spend a lot of time to choose travel services that match their needs because of the complex amount of relevant information. Under such circumstances, this paper studied the recommendation method in travel platform. First, the big data is used to extract user data. Secondly, the current online travel business recommendation for users has the problem of low accuracy. The reason is that the services provided are still in traditional recommendation algorithm. In this paper, the Bayesian network is used to evaluate the user’s attribute preference and generate a data model, using effective methods in artificial intelligence algorithms to improve collaborative filtering algorithms and finally generate hybrid recommendation algorithms. Compared with the traditional recommendation method, the experimental results showed that the research can improve the recommendation accuracy of tourist attractions by 6.55%, increase the user’s satisfaction for the platform, and enhance the visit rate and retention rate of the tourist attraction recommendation platform.