Energy Efficient Medical Data Dimensionality Reduction using Optimized Principal Component Analysis

INTRODUCTION: The method of minimizing the number of random variables or attributes from the enormous data set is the reduction of dimensionality. The space available for storing the database is therefore minimized by decreasing the scale of the features. OBJECTIVES: The PCA algorithm is used...

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Autores principales:	S. Sophia, K. Thanammal, S. Sujatha
Formato:	article
Lenguaje:	EN
Publicado:	European Alliance for Innovation (EAI) 2022
Materias:	principal component analysis and tree seed optimization algorithm Science Q Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/f7e660bb57134f518b3750de86fc828b
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spelling	oai:doaj.org-article:f7e660bb57134f518b3750de86fc828b2021-11-30T11:07:32ZEnergy Efficient Medical Data Dimensionality Reduction using Optimized Principal Component Analysis2032-944X10.4108/eai.6-8-2021.170667https://doaj.org/article/f7e660bb57134f518b3750de86fc828b2022-01-01T00:00:00Zhttps://eudl.eu/pdf/10.4108/eai.6-8-2021.170667https://doaj.org/toc/2032-944XINTRODUCTION: The method of minimizing the number of random variables or attributes from the enormous data set is the reduction of dimensionality. The space available for storing the database is therefore minimized by decreasing the scale of the features. OBJECTIVES: The PCA algorithm is used to achieve dimensional reduction by deep learning to recover image characteristics. This approach is designed to reduce the dimensionality of such datasets, improve interpretability while minimizing the loss of information.METHODS: The dimensionality reduction of the method by using optimized PCA algorithm. The input data set can be reducing the dimension by using PCA algorithm. The tree seed optimization algorithm (TSO) can be utilized to select the optimal data’s in PCA algorithms. After completing the TSO-PCA the new data set are created by the reduced dimensions. RESULTS: The input data and images are used to reduce the dimension based on the TSO-PCA algorithms. The simulations for obtaining the results were carried out using python. The results of the feature dimensionality reduction on DIABETES dataset and Indian pines dataset. CONCLUSION: The best data for the data collection, the TSO algorithm is used and the PCA algorithm is used to minimize the dimensions. The suggested method is better than the existing method compared to the linear, kernel, random basic function, and polynomial for evaluating the outcome and discussion. In order to improve accuracy in future work, we will continue research and try to find more advanced techniques for this problem.S. SophiaK. ThanammalS. SujathaEuropean Alliance for Innovation (EAI)articleprincipal component analysis and tree seed optimization algorithmScienceQMathematicsQA1-939Electronic computers. Computer scienceQA75.5-76.95ENEAI Endorsed Transactions on Energy Web, Vol 9, Iss 37 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	principal component analysis and tree seed optimization algorithm Science Q Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95
spellingShingle	principal component analysis and tree seed optimization algorithm Science Q Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95 S. Sophia K. Thanammal S. Sujatha Energy Efficient Medical Data Dimensionality Reduction using Optimized Principal Component Analysis
description	INTRODUCTION: The method of minimizing the number of random variables or attributes from the enormous data set is the reduction of dimensionality. The space available for storing the database is therefore minimized by decreasing the scale of the features. OBJECTIVES: The PCA algorithm is used to achieve dimensional reduction by deep learning to recover image characteristics. This approach is designed to reduce the dimensionality of such datasets, improve interpretability while minimizing the loss of information.METHODS: The dimensionality reduction of the method by using optimized PCA algorithm. The input data set can be reducing the dimension by using PCA algorithm. The tree seed optimization algorithm (TSO) can be utilized to select the optimal data’s in PCA algorithms. After completing the TSO-PCA the new data set are created by the reduced dimensions. RESULTS: The input data and images are used to reduce the dimension based on the TSO-PCA algorithms. The simulations for obtaining the results were carried out using python. The results of the feature dimensionality reduction on DIABETES dataset and Indian pines dataset. CONCLUSION: The best data for the data collection, the TSO algorithm is used and the PCA algorithm is used to minimize the dimensions. The suggested method is better than the existing method compared to the linear, kernel, random basic function, and polynomial for evaluating the outcome and discussion. In order to improve accuracy in future work, we will continue research and try to find more advanced techniques for this problem.
format	article
author	S. Sophia K. Thanammal S. Sujatha
author_facet	S. Sophia K. Thanammal S. Sujatha
author_sort	S. Sophia
title	Energy Efficient Medical Data Dimensionality Reduction using Optimized Principal Component Analysis
title_short	Energy Efficient Medical Data Dimensionality Reduction using Optimized Principal Component Analysis
title_full	Energy Efficient Medical Data Dimensionality Reduction using Optimized Principal Component Analysis
title_fullStr	Energy Efficient Medical Data Dimensionality Reduction using Optimized Principal Component Analysis
title_full_unstemmed	Energy Efficient Medical Data Dimensionality Reduction using Optimized Principal Component Analysis
title_sort	energy efficient medical data dimensionality reduction using optimized principal component analysis
publisher	European Alliance for Innovation (EAI)
publishDate	2022
url	https://doaj.org/article/f7e660bb57134f518b3750de86fc828b
work_keys_str_mv	AT ssophia energyefficientmedicaldatadimensionalityreductionusingoptimizedprincipalcomponentanalysis AT kthanammal energyefficientmedicaldatadimensionalityreductionusingoptimizedprincipalcomponentanalysis AT ssujatha energyefficientmedicaldatadimensionalityreductionusingoptimizedprincipalcomponentanalysis
_version_	1718406720280068096

Energy Efficient Medical Data Dimensionality Reduction using Optimized Principal Component Analysis

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