Affiliation:
1. Computer Science, and Enginering USICT GGSIPU, Delhi
Abstract
A Neural Network is one of the techniques by which we classify data. In this paper, we have proposed an effectively stacked autoencoder with the help of a modified sigmoid activation function. We have made a two-layer stacked autoencoder with a modified sigmoid activation function. We have compared our autoencoder to the existing autoencoder technique. In the existing autoencoder technique, we generally use the logsigmoid activation function. But in multiple cases using this technique, we cannot achieve better results. In that case, we may use our technique for achieving better results. Our proposed autoencoder may achieve better results compared to this existing autoencoder technique. The reason behind this is that our modified sigmoid activation function gives more variations for different input values. We have tested our proposed autoencoder on the iris, glass, wine, ovarian, and digit image datasets for comparison propose. The existing autoencoder technique has achieved 96% accuracy on the iris, 91% accuracy on wine, 95.4% accuracy on ovarian, 96.3% accuracy on glass, and 98.7% accuracy on digit (image) dataset. Our proposed autoencoder has achieved 100% accuracy on the iris, wine, ovarian, and glass, and 99.4% accuracy on digit (image) datasets. For more verification of the effeteness of our proposed autoencoder, we have taken three more datasets. They are abalone, thyroid, and chemical datasets. Our proposed autoencoder has achieved 100% accuracy on the abalone and chemical, and 96% accuracy on thyroid datasets.
Subject
Artificial Intelligence,General Engineering,Statistics and Probability
Reference45 articles.
1. Extraction of dynamic operationstrategy for standalone solar-based multi-energy systems: A methodbased on decision tree algorithm,};Luo;{Sustainable Cities andSociety,2021
2. Convolutional and recurrentneural networks for the detection of valvular heart diseases inphonocardiogram recordings,;Alkhodari;Computer Methods and Programs inBiomedicine,2021
3. K-means clustering and kNN classification based on negative databases,;Zhao;Applied Soft Computing,2021
4. Predicting stock market price of Bangladesh: a comparative study of linear classification models,;Karimuzzaman;Annals of Data Science,2021
5. Comparison and analysis of logisticregression, Naïve Bayes and KNN machine learning algorithms forcredit card fraud detection,;Itoo;International Journal ofInformation Technology,2021
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献