The strategy to simulate the cross-pollination rate for the co-existence of genetically modified (GM) and non-GM crops by using FPSOSVR

Lee, Chien-Pang; Shieh, G. J.; Yiu, Tien-Joung; Kuo, Bo‐Jein

doi:10.1016/j.chemolab.2013.01.002

Cited by 3 publications

(5 citation statements)

References 17 publications

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“…The extended SVM model is also called the SVR model. The SVR model has been successfully applied in agricultural research [13,14,26,27,[36][37][38]. The algorithm of the SVR model can be summarized as follows.…”

Section: Support Vector Regressionmentioning

confidence: 99%

“…Although many statistical models are applied in agricultural fields, this work is still a challenge because such statistical models should be based on linear assumptions [12,13]. Therefore, many researchers have also used data mining, machine learning, artificial intelligence, and hybrid models to predict agricultural data [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…Although many models have been successfully applied in agricultural fields, some problems still exist; for example, (a) agricultural output data might not satisfy statistical assumptions [12,13,21,22] because annual agricultural output data are highly volatile; and (b) prediction models have to be frequently retrained to satisfy different conditions and enhance the prediction performance [23,24]. This study proposes a robust machine-learning model for long-term agricultural output prediction to avoid these problems.…”

Section: Introductionmentioning

confidence: 99%

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The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model

et al. 2022

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Recently, annual agricultural data have been highly volatile as a result of climate change and national economic trends. Therefore, such data might not be enough to develop good agricultural policies for stabilizing agricultural output. A good agricultural output prediction model to assist agricultural policymaking has thus become essential. However, the highly volatile data would affect the prediction model’s performance. For this reason, this study proposes a marriage in honey bees optimization/support vector regression (MBO/SVR) model to minimize the effects of highly volatile data (outliers) and enhance prediction accuracy. We verified the performance of the MBO/SVR model by using the annual total agricultural output collected from the official Agricultural Statistics Yearbook of the Council of Agriculture, Taiwan. Taiwan’s annual total agricultural output integrates agricultural, livestock and poultry, fishery, and forest products. The results indicated that the MBO/SVR model had a lower mean absolute percentage error (MAPE), root mean square percentage error (RMSPE), and relative root mean squared error (r-RMSE) than those of the models it was compared to. Furthermore, the MBO/SVR model predicted long-term agricultural output more accurately and achieved higher directional symmetry (DS) than the other models. Accordingly, the MBO/SVR model is a robust, high-prediction-accuracy model for predicting long-term agricultural output to assist agricultural policymaking.

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Section: Support Vector Regressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model

et al. 2022

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“…The concept of the ANN model was first introduced in the 1950s [22]. Now, hundreds of different ANN models have been developed.…”

Section: Artificial Neural Network Modelmentioning

confidence: 99%

“…The framework of an ANN model, as shown in Figure 2, contains three layers: the input layer, the hidden layer and the output layer. Recently, the radial basis function neural network model has been successfully applied in many financial issue applications [8,14,22]. We use the radial basis function neural network model as the main comparison model in this paper.…”

Section: Artificial Neural Network Modelmentioning

confidence: 99%

The strategy of investment in the stock market using modified support vector regression model

2017

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Abstract. Stock indices forecasting has become a popular research issue in recent years.Although many statistical time series models have been applied in stock indices forecasting, they are limited to certain assumptions. Accordingly, the traditional statistical time series models might not be suitable for forecasting real-life stock indices data. Hence, this paper proposes a novel forecasting model to assist investors in determining a strategy for investments in the stock market. The proposed model is called the modified support vector regression model, which is composed of the correlation coefficient method, the sliding window algorithm and the support vector regression model. The results show that the forecasting accuracy of the proposed model is more stable than the existing models in terms of average and standard deviation of the root mean square error (RMSE) and the mean absolute percentage error (MAPE). Accordingly, the proposed model would be used to assist investors in determining a strategy for investing in stocks.

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Using hybrid support vector regression to predict agricultural output

Jheng

Lee

2018

2018 27th Wireless and Optical Communication Conference (WOCC)

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The strategy to simulate the cross-pollination rate for the co-existence of genetically modified (GM) and non-GM crops by using FPSOSVR

Cited by 3 publications

References 17 publications

The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model

The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model

The strategy of investment in the stock market using modified support vector regression model

Using hybrid support vector regression to predict agricultural output

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