A NEW CROP YIELD PREDICTION SYSTEM USING RANDOM FOREST COMBINED WITH LEAST SQUARES SUPPORT VECTOR MACHINE

Yield forecasting is based totally entirely on soil, water and vegetation to be a possible subject. Deep-based depth-based fashions are widely accustomed extract important plant functions for predictive purposes. Although such strategies are necessary to resolve the matter of predicting yields there are the subsequent abnormalities: they can't create an indirect or indirect map between raw facts and yield values; and also the full functionality of this excess is explained within the high satisfaction of the published works. Deep durability provides guidance and motivation for the above-mentioned errors. Combining master intensity and deep mastering, deep reinforcing mastering creates a comprehensive yield prediction framework which will plan the uncooked facts in crop prediction rates. The proposed project creates a version of the Deep Recurrent Q-Network Support Vector Machine deep mastering set of rules over Q-Learning to strengthen the mastering set of rules for predicting yield. Sequential downloads of the Recurrent Neural community are fed by fact parameters. The Q-mastering community creates a predictive yield environment based totally on input criteria. The precise layer displays the discharge values of the Support Vector Machine on the Q values. The reinforcement master component contains a mix of parametric functions on the sting that helps predict the yield. Finally, the agent obtains a measure of the mixture of steps performed by minimizing the error and increasing the accuracy of the forecast. The proposed model successfully predicts this crop yield that's hip by keeping the initial distribution of facts with 93.7% accuracy.

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Best Fit Radial Kernel Support Vector Machine for Intelligent Crop Yield Prediction Method

Machine Learning and Information Processing - Advances in Intelligent Systems and Computing ◽

10.1007/978-981-33-4859-2_45 ◽

2021 ◽

pp. 457-467

Author(s):

Vijay Hanuman ◽

Krishna Vamsi Pinnamaneni ◽

Tripty Singh

Keyword(s):

Support Vector Machine ◽

Crop Yield ◽

Prediction Method ◽

Support Vector ◽

Yield Prediction ◽

Kernel Support Vector Machine ◽

Best Fit ◽

Radial Kernel

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Applying Government Schemes in Rural Sectors Prediction System for Evaluation of Data Science Algorithm

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.e5140.019521 ◽

2021 ◽

Vol 9 (5) ◽

pp. 70-79

Author(s):

S. Maheswari ◽

S. Kalaiselvi ◽

D.Thamarai Selvi ◽

M. Manochitra

Keyword(s):

Support Vector Machine ◽

Random Forest ◽

Environmental Factors ◽

Data Science ◽

Naive Bayes ◽

Support Vector ◽

Prediction System ◽

Government Assistance ◽

The Government ◽

Evaluation Of Data

The administration dispatches different aggressive projects attempting to make the nation more prosperous, yet what they bomb is in fruitful execution and coming to recipients. The fundamental explanation for this issue is the absence of mindfulness among rustic individuals. This paper is to give an answer for this uninformed circumstance. Through this framework the rustic understudies will be instructed such that they can become acquainted with about what are the different plans that are outfitted by the administration and what are the plans they are qualified for. On the off chance that the country understudies came to know and get mindful of the apparent multitude of legislative plans gave by the Government of India for the government assistance of the provincial understudies, at that point their life would venture into next level. At first this framework will investigate the accessible government plans in the instructive for the government assistance of country understudies. Next, the understudy's information ((i.e.) name, age, station, occupation, annualincome.etc) are accumulated. At that point; both the datasets are brought into the Anaconda Navigator. At that point, investigation and grouping dependent on networks (SC, ST, BC and MBC) of the understudies and the plans are performed. At that point utilizing the forecast calculations (Naïve Bayes, Random Forest and Support Vector Machine (SVM)) what are generally the plans the specific understudy is qualified for are anticipated. An investigation is made on the proficiency of the three calculations. The exactness of the three calculations is broke down and the effective calculation which creates the outcome with most elevated precision is at last used to play out the forecast of the plans that a specific understudy is qualified for. At long last, the anticipated plans anticipated utilizing the most elevated effective calculation among the three calculations will be gotten back to the understudies. Hence, through this undertaking the rustic understudies will come to think about different recipient plans gave by government and they can use those plans for the improvement of the country environmental factors

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Investigating the use of random forest, gradient boosting machine, support vector machine and their ensemble applied to fault detection

10.26678/abcm.cobem2017.cob17-1600 ◽

2017 ◽

Author(s):

Luis Felipe Nogoseke ◽

Gabriel Herman Bernardim Andrade ◽

Marco Boaretto ◽

Leandro Coelho

Keyword(s):

Support Vector Machine ◽

Random Forest ◽

Fault Detection ◽

Gradient Boosting ◽

Support Vector ◽

Gradient Boosting Machine

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Q-learning System Based on Cooperative Least Squares Support Vector Machine

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2009.00214 ◽

2009 ◽

Vol 35 (2) ◽

pp. 214-219 ◽

Cited By ~ 3

Author(s):

Xue-Song WANG ◽

Xi-Lan TIAN ◽

Yu-Hu CHENG ◽

Jian-Qiang YI

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Learning System ◽

Support Vector ◽

Q Learning

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Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

Mathematical Problems in Engineering ◽

10.1155/2020/8642430 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Shengpu Li ◽

Yize Sun

Keyword(s):

Support Vector Machine ◽

Prediction Model ◽

Least Squares ◽

Transfer Rate ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Experimental Sample ◽

Machine Model ◽

Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

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