Development of accurate classification of heavenly bodies using novel machine learning techniques
Article
Wierzbinski, Michal, Pławiak, Pawel, Hammad, Mohamed and Acharya, U. Rajendra. 2021. "Development of accurate classification of heavenly bodies using novel machine learning techniques." Soft Computing. 25 (10), pp. 7213-7228. https://doi.org/10.1007/s00500-021-05687-4
| Article Title | Development of accurate classification of heavenly bodies using novel machine learning techniques |
|---|---|
| ERA Journal ID | 36486 |
| Article Category | Article |
| Authors | Wierzbinski, Michal, Pławiak, Pawel, Hammad, Mohamed and Acharya, U. Rajendra |
| Journal Title | Soft Computing |
| Journal Citation | 25 (10), pp. 7213-7228 |
| Number of Pages | 16 |
| Year | 2021 |
| Publisher | Springer |
| Place of Publication | Germany |
| ISSN | 1432-7643 |
| 1433-7479 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s00500-021-05687-4 |
| Web Address (URL) | https://link.springer.com/article/10.1007/s00500-021-05687-4 |
| Abstract | The heavenly bodies are objects that swim in the outer space. The classification of these objects is a challenging task for astronomers. This article presents a novel methodology that enables an efficient and accurate classification of cosmic objects (3 classes) based on evolutionary optimization of classifiers. This research collected the data from Sloan Digital Sky Survey database. In this work, we are proposing to develop a novel machine learning model to classify stellar spectra of stars, quasars and galaxies. First, the input data are normalized and then subjected to principal component analysis to reduce the dimensionality. Then, the genetic algorithm is implemented on the data which helps to find the optimal parameters for the classifiers. We have used 21 classifiers to develop an accurate and robust classification with fivefold cross-validation strategy. Our developed model has achieved an improvement in the accuracy using nineteen out of twenty-one models. We have obtained the highest classification accuracy of 99.16%, precision of 98.78%, recall of 98.08% and F1-score of 98.32% using evolutionary system based on voting classifier. The developed machine learning prototype can help the astronomers to make accurate classification of heavenly bodies in the sky. Proposed evolutionary system can be used in other areas where accurate classification of many classes is required. |
| Keywords | Facial expression recognition ; Exemplar deep feature ; Neighbor component analysis ; Emotion detection |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Byline Affiliations | Spyrosoft, Poland |
| Cracow University of Technology, Poland | |
| Polish Academy of Sciences, Poland | |
| Menoufia University, Egypt | |
| Ngee Ann Polytechnic, Singapore | |
| Asia University, Taiwan | |
| Singapore University of Social Sciences (SUSS), Singapore |
Permalink -
https://research.usq.edu.au/item/z1w46/development-of-accurate-classification-of-heavenly-bodies-using-novel-machine-learning-techniques
Download files
45
total views18
total downloads4
views this month0
downloads this month