Forecasting of China’s solar PV industry installed capacity and analyzing of employment effect: based on GRA-BiLSTM model

Liu, Bingchun; Song, Chengyuan; Wang, Qingshan; Wang, Yuan

doi:10.1007/s11356-021-15957-1

Cited by 25 publications

(15 citation statements)

References 39 publications

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“…To substantiate the performance of the proposed voting classifier, it is also compared with deep learning models. We have used three deep learning models for experiments including LSTM 53 , CNN 54 , CNN-LSTM 56 , and BiLSTM 55 for comparison purposes. Layered architecture and hyperparameter values are presented in Figure 8 .…”

Section: Resultsmentioning

confidence: 99%

Predicting death risk analysis in fully vaccinated people using novel extreme regression-voting classifier

et al. 2022

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Vaccination for the COVID-19 pandemic has raised serious concerns among the public and various rumours are spread regarding the resulting illness, adverse reactions, and death. Such rumours can damage the campaign against the COVID-19 and should be dealt with accordingly. One prospective solution is to use machine learning-based models to predict the death risk for vaccinated people by utilizing the available data. This study focuses on the prognosis of three significant events including ‘not survived’, ‘recovered’, and ‘not recovered’ based on the adverse events followed by the second dose of the COVID-19 vaccine. Extensive experiments are performed to analyse the efficacy of the proposed Extreme Regression- Voting Classifier model in comparison with machine learning models with Term Frequency-Inverse Document Frequency, Bag of Words, and Global Vectors, and deep learning models like Convolutional Neural Network, Long Short Term Memory, and Bidirectional Long Short Term Memory. Experiments are carried out on the original, as well as, a balanced dataset using Synthetic Minority Oversampling Approach. Results reveal that the proposed voting classifier in combination with TF-IDF outperforms with a 0.85 accuracy score on the SMOTE-balanced dataset. In line with this, the validation of the proposed voting classifier on binary classification shows state-of-the-art results with a 0.98 accuracy.

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“…To substantiate the performance of the proposed voting classifier, it is also compared with deep learning models. We have used three deep learning models for experiments including LSTM 53 , CNN 54 , CNN-LSTM 56 , and BiLSTM 55 for comparison purposes. Layered architecture and hyperparameter values are presented in Figure 8 .…”

Section: Resultsmentioning

confidence: 99%

Predicting death risk analysis in fully vaccinated people using novel extreme regression-voting classifier

et al. 2022

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Section: Resultsmentioning

confidence: 99%

“…To substantiate the performance of the proposed voting classifier, it is also compared with deep learning models. We have used three deep learning models for experiments including LSTM [57], CNN [58] and BiLSTM [59] Classification results of deep learning models on balanced and imbalanced datasets are presented in Table 13. It can be observed that LSTM achieves the highest result with a 0.70 value of accuracy, precision, recall, and F1 score on imbalanced data while CNN has shown the lowest results.…”

Section: Performance Comparison With Deep Neural Networkmentioning

confidence: 99%

Novel extreme regression-voting classifier to predict death risk in vaccinated people using VAERS data

et al. 2022

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COVID-19 vaccination raised serious concerns among the public and people are mind stuck by various rumors regarding the resulting illness, adverse reactions, and death. Such rumors are dangerous to the campaign against the COVID-19 and should be dealt with accordingly and timely. One prospective solution is to use machine learning-based models to predict the death risk for vaccinated people and clarify people’s perceptions regarding death risk. This study focuses on the prediction of the death risks associated with vaccinated people followed by a second dose for two reasons; first to build consensus among people to get the vaccines; second, to reduce the fear regarding vaccines. Given that, this study utilizes the COVID-19 VAERS dataset that records adverse events after COVID-19 vaccination as ‘recovered’, ‘not recovered’, and ‘survived’. To obtain better prediction results, a novel voting classifier extreme regression-voting classifier (ER-VC) is introduced. ER-VC ensembles extra tree classifier and logistic regression using soft voting criterion. To avoid model overfitting and get better results, two data balancing techniques synthetic minority oversampling (SMOTE) and adaptive synthetic sampling (ADASYN) have been applied. Moreover, three feature extraction techniques term frequency-inverse document frequency (TF-IDF), bag of words (BoW), and global vectors (GloVe) have been used for comparison. Both machine learning and deep learning models are deployed for experiments. Results obtained from extensive experiments reveal that the proposed model in combination with TF-TDF has shown robust results with a 0.85 accuracy when trained on the SMOTE-balanced dataset. In line with this, validation of the proposed voting classifier on binary classification shows state-of-the-art results with a 0.98 accuracy. Results show that machine learning models can predict the death risk with high accuracy and can assist the authors in taking timely measures.

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“…This article mainly reviews the indirect effects of industrial green development on employment structure. Clean energy creates jobs, increases employment opportunities for high-skilled workers (Ibrahiem and Sameh, 2020;Liu et al, 2022), and optimizes the employment structure (Zhang et al, 2017). Similarly, environmental regulation improves the work environment , reduces polluting production, controls pollution, and develops clean technologies (Li and Du, 2022) to optimize employment structure.…”

Section: Introductionmentioning

confidence: 99%

Study on the impact of industrial green development and technological innovation on employment structure

Hu²,

Zhao³

2023

Front. Earth Sci.

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Exploring the relationship between industrial green development, technological innovation, and employment structure, especially the impact between industrial green development and technological innovation on employment structure, is of enormous theoretical and practical importance to achieve high-quality employment as well as optimize the employment structure of China. Thirty Chinese provinces’ data from 2009 to 2019 is adopted to assess industrial green development levels. Considering the above, this research innovatively integrates industrial green development, technological innovation, and employment structure into an analytical framework, and empirically investigates the effects of the two factors and their interaction on employment structure by adopting a two-way fixed effects model. The specific conclusions are presented as follows. Firstly, China’s industrial green development levels exhibit a fluctuating and rising time-series evolutionary feature from 2009 to 2019 and have regional differences. Secondly, industrial green development, technological innovation, and their interaction are conducive to optimizing China’s employment structure. Thirdly, the eastern and northeastern areas’ employment structure optimization is boosted by industrial green development. However, the corresponding regression coefficients in the western and central areas are not significant. The northeastern, eastern, and western areas’ technological innovation encourages employment structure improvement. Instead, the employment structure is hampered by technological innovation in the central region. An interaction between industrial green evolution and technological innovation positively affects relevant employment structures in the four regions. Specific results of this research are of necessary theoretical significance and the realistic reference price for whether industrial green development and the interplay affect employment structure.

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Forecasting of China’s solar PV industry installed capacity and analyzing of employment effect: based on GRA-BiLSTM model

Cited by 25 publications

References 39 publications

Predicting death risk analysis in fully vaccinated people using novel extreme regression-voting classifier

Predicting death risk analysis in fully vaccinated people using novel extreme regression-voting classifier

Novel extreme regression-voting classifier to predict death risk in vaccinated people using VAERS data

Study on the impact of industrial green development and technological innovation on employment structure

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