An intelligent scheme for big data recovery in Internet of Things based on Multi-Attribute assistance and Extremely randomized trees

Cheng, Hongju; Shi, Yushi; Wu, Lingying; Guo, Yingya; Xiong, Naixue

doi:10.1016/j.ins.2020.12.041

Cited by 26 publications

(8 citation statements)

References 38 publications

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“… Halverson and Graham (2019) added that personal and contextual facilitators of engagement, including learner characteristics and thoughtful learning experience design, can increase the likelihood of learner engagement, and learner engagement in blended learning environments will support advances in blended learning engagement research that is increasingly real-time, minimally intrusive, and maximally generalizable across subject matter contexts ( Cheng et al, 2018a , b , 2021a , 2021b ; Cheng R. et al, 2019 ; Cheng Y. et al, 2019 ; Chen et al, 2020 , 2021 ; Dai et al, 2020 ).…”

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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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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“…The efficiency improvement is shown with the use of machine learning methods. (22) Moreover, a deep learning strategy with the use of convolutional neural network (CNN) for data recovery in health monitoring WSN. (23) The structured data communication pattern is considered in this method.…”

Section: Literature Surveymentioning

confidence: 99%

Machine learning based efficient routing protocol in wireless sensor network

Madkar

Pardeshi²,

Kumbhar

2022

Salud, Ciencia y Tecnología

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Data loss and recovery are important factors that directly affect the efficiency of the wireless sensor network (WSN). The wireless channel characteristics have a significant impact on data transmission and reception. On the receiver side, the most difficult tasks are maximizing packet delivery ratio and recovering lost data. In some cases, cyclic redundancy check (CRC) based algorithms can provide better data recovery. The CRC method can be made adaptive by using channel characteristics to correct the error bits. This paper evaluates the performance of the proposed machine learning-based efficient routing protocol (ML-ERP). For data recovery, the CRC with channel impulse response (CIR) prediction based on sensor node location information was used. The data recovery capability of ML-ERP increased the network efficiency in terms of packet delivery ratio. Also, due to less data loss, the energy efficiency of the network was also improved by almost 6 % ove existing protocols.

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“…The difference between the ERT and RF is that the ERT uses all the samples to construct a decision tree in the training process. For node splitting, the RF algorithm selects the best attribute split, while the ERT randomly selects the attribute split [38], which results in the size of the generated decision tree being larger than that generated by the RF model. Therefore, the variance of the ERT model is reduced compared to the RF model.…”

Section: Extremely Randomized Trees (Ert)mentioning

confidence: 99%

Retrieval of Water Quality from UAV-Borne Hyperspectral Imagery: A Comparative Study of Machine Learning Algorithms

Wei

et al. 2021

Remote Sensing

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The rapidly increasing world population and human activities accelerate the crisis of the limited freshwater resources. Water quality must be monitored for the sustainability of freshwater resources. Unmanned aerial vehicle (UAV)-borne hyperspectral data can capture fine features of water bodies, which have been widely used for monitoring water quality. In this study, nine machine learning algorithms are systematically evaluated for the inversion of water quality parameters including chlorophyll-a (Chl-a) and suspended solids (SS) with UAV-borne hyperspectral data. In comparing the experimental results of the machine learning model on the water quality parameters, we can observe that the prediction performance of the Catboost regression (CBR) model is the best. However, the prediction performances of the Multi-layer Perceptron regression (MLPR) and Elastic net (EN) models are very unsatisfactory, indicating that the MLPR and EN models are not suitable for the inversion of water quality parameters. In addition, the water quality distribution map is generated, which can be used to identify polluted areas of water bodies.

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An intelligent scheme for big data recovery in Internet of Things based on Multi-Attribute assistance and Extremely randomized trees

Cited by 26 publications

References 38 publications

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

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

Machine learning based efficient routing protocol in wireless sensor network

Retrieval of Water Quality from UAV-Borne Hyperspectral Imagery: A Comparative Study of Machine Learning Algorithms

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