Ensemble Learning Based on GBDT and CNN for Adoptability Prediction

Ye, Yunfan; Liu, Fang; Shu, Zhao; Liang, Zhiyao

doi:10.32604/cmc.2020.011632

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…First is data absoluteness, implying when network methods are performed for clarification, it will be much easier if the data is task-related, organized, and rich. The second feature is its effectiveness and appropriateness in describing complex connections between features and the target variable (Ye et al 2020 ). To improve the performance of LSTM RNNs, we have to do more training on a model to increase the accuracy of results.…”

Section: Introductionmentioning

confidence: 99%

Groundwater quality prediction based on LSTM RNN: An Iranian experience

Valadkhan

Moghaddasi

Mohammadinejad

2022

Int. J. Environ. Sci. Technol.

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Section: Introductionmentioning

confidence: 99%

Groundwater quality prediction based on LSTM RNN: An Iranian experience

Valadkhan

Moghaddasi

Mohammadinejad

2022

Int. J. Environ. Sci. Technol.

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“…As a result, because of the lack of information due to the loss of image spatial information, the ANN is inefficient in extracting and learning features and has limitations in improving accuracy. A CNN is a model that can be trained while maintaining the spatial information of the image to compensate for this problem [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A Study on Classification and Detection of Small Moths Using CNN Model

Hilal¹,

Alsolai²,

Al‐Wesabi³

et al. 2022

Computers, Materials &Amp; Continua

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Currently, there are many limitations to classify images of small objects. In addition, there are limitations such as error detection due to external factors, and there is also a disadvantage that it is difficult to accurately distinguish between various objects. This paper uses a convolutional neural network (CNN) algorithm to recognize and classify object images of very small moths and obtain precise data images. A convolution neural network algorithm is used for image data classification, and the classified image is transformed into image data to learn the topological structure of the image. To improve the accuracy of the image classification and reduce the loss rate, a parameter for finding a fast-optimal point of image classification is set by a convolutional neural network and a pixel image as a preprocessor. As a result of this study, we applied a convolution neural network algorithm to classify the images of very small moths by capturing precise images of the moths. Experimental results showed that the accuracy of classification of very small moths was more than 90%.

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Predicting video views of web series based on comment sentiment analysis and improved stacking ensemble model

Wulandari

2022

Electron Commer Res

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Ensemble Learning Based on GBDT and CNN for Adoptability Prediction

Cited by 6 publications

References 26 publications

Groundwater quality prediction based on LSTM RNN: An Iranian experience

Groundwater quality prediction based on LSTM RNN: An Iranian experience

A Study on Classification and Detection of Small Moths Using CNN Model

Predicting video views of web series based on comment sentiment analysis and improved stacking ensemble model

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