Relevance feedback based online learning model for resource bottleneck prediction in cloud servers

Gupta, Shaifu; Dileep, A. D.

doi:10.1016/j.neucom.2020.04.080

Cited by 8 publications

(5 citation statements)

References 24 publications

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“…In the process of processing image data by artificial intelligence, there are several main problems as follows: Data transmission is limited by the network. The huge amount of data generated at the edge is uploaded to the cloud for processing, and then the processing results are returned from the cloud, which not only wastes a lot of network resources but also consumes a lot of time and causes a lot of network latency; Data processing is limited by cloud server computing bottlenecks [ 22 ]. It is still unable to meet the demand of the exploding data computing and processing.…”

Section: Concepts Related To Artificial Intelligence Dance Movement R...mentioning

confidence: 99%

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Research on Multimodal Dance Movement Recognition Based on Artificial Intelligence Image Technology

Zeng

2022

Computational Intelligence and Neuroscience

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At present, most robot dances are precompiled. Changing music requires manual adjustment of relevant parameters and metamovements, which greatly reduces the fun and intelligence. In view of the above problems, this paper designed CNN system, studied the multimodal dance movement recognition algorithm of artificial intelligence image technology, and completed the construction of a multimodal dance movement calculation system example. The results show that the CNN algorithm and the Winograd algorithm-based coprocessor-optimized CNN network in multimodal dance movement recognition with image technology reduce from a maximum of 132s to 26s in the runtime criterion, with a maximum reduction of 80%; from a maximum of 73.5% to 16.2% in the memory access criterion, with a maximum reduction of 57.3%; and from a maximum of 93.6% to 25.2% in the power consumption ratio criterion, with a maximum reduction of 68.4%. In the power consumption ratio criterion, the maximum reduction from 93.6% to 25.2% is 68.4%. The maximum accuracy of the proposed optimization method is 95.1%. The solution is proposed to address the problem of insufficient performance of traditional dance movement recognition, which will contribute to the development of artificial intelligence and dance industry.

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Section: Concepts Related To Artificial Intelligence Dance Movement R...mentioning

confidence: 99%

“…(2) Data processing is limited by cloud server computing bottlenecks [22]. It is still unable to meet the demand of the exploding data computing and processing.…”

Section: Concepts Related To Artificial Intelligence Dance Movement R...mentioning

confidence: 99%

Research on Multimodal Dance Movement Recognition Based on Artificial Intelligence Image Technology

Zeng

2022

Computational Intelligence and Neuroscience

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“…Where C 1 , C 2 , C 3 are coefficients that can be calculated according to formula (5) to obtain specific values.…”

Section: Related Theories a Gray Wolf Optimization Algorithmmentioning

confidence: 99%

“…However, the peak period of cloud service usage usually has a certain regularity in a long term. Through the analysis and mining of historical data, the resource load prediction for a period in the future can be realized [5] , to provide a basis for resource scheduling and ensure the service performance and quality of the cloud server.…”

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confidence: 99%

Research on Optimization of GWO-BP Model for Cloud Server Load Prediction

Hou

Guo²,

et al. 2021

IEEE Access

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“…The application of these media can be implemented in various methods. Media in digital form can be applied to online learning effectively (Jeffrey Alan Gupta & Dileep, 2020;Kim, Wang, & Ketenci, 2019;Rusli, Rahman, & Abdullah, 2020). Online learning with appropriate media content can improve the output of learning outcomes.…”

Section: Introductionmentioning

confidence: 99%

The Effectiveness of Archiving Videos and Online Learning on Student’s Learning and Innovation Skills

Indrawati¹

2021

INT J INSTRUCTION

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This study to analyze the level of relationship and the effect of archiving video media and online learning on students' learning and innovation skills. The research method used was a quantitative approach with a questionnaire instrument. The questionnaire was distributed to vocational students who attended the archiving subject using archiving video media that had been previously developed by the researcher. The data analysis involved prerequisite test analysis including validity and reliability tests. Furthermore, the data had to meet one of the assumption tests, which was normality. The data were analyzed using a multiple linear regression model. The results showed that (1) the use of archiving video media and online learning had a positive and significant effect on students' learning and innovation skills simultaneously, (2) the use of archiving video media had a positive and significant effect on students' learning and innovation skills, and (3) online learning had a positive and significant impact on students' learning and innovation skills. These results showed that the use of archiving video media and online learning was proven to be able to affect students' learning and innovation skills so that they can become a reference for further research.

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Relevance feedback based online learning model for resource bottleneck prediction in cloud servers

Cited by 8 publications

References 24 publications

Research on Multimodal Dance Movement Recognition Based on Artificial Intelligence Image Technology

Research on Multimodal Dance Movement Recognition Based on Artificial Intelligence Image Technology

Research on Optimization of GWO-BP Model for Cloud Server Load Prediction

The Effectiveness of Archiving Videos and Online Learning on Student’s Learning and Innovation Skills

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