| Autor: |
Prasad, C. Guru, Adimoolam, M. |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2023, Vol. 2822 Issue 1, p1-8, 8p |
| Abstrakt: |
The main aim of this research article is to predict the share market stock price to improve accuracy and precision by using Convolutional Neural Network (CNN) in comparison with Support Vector Machine (SVM). The dataset in this paper utilizes the publicly available dataset from National Stock Exchange (NSE) to prove the effectiveness of the approach. The sample size to predict the share market stock price to improve accuracy and precision was sample 280 (Group 1=140 and Group 2 =140) and calculation is performed utilizing G-power 0.8 with alpha and beta qualities are 0.05, 0.2 with a confidence interval at 95%. The prediction of share market stock price to improve accuracy and precision is performed by CNN whereas number of samples (N=10) and SVM where number of samples (N=10). The CNN classifier has a 95.68% higher accuracy rate when compared to the accuracy rate of the SVM is 91.25 %. The study has a significance value of p<0.05 i.e. p=0.0271. CNN provides better outcomes in accuracy rate when compared to SVM for prediction of share market stock price to improve accuracy and precision. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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